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23 pages, 5469 KiB  
Article
A Wide-Range Adjustable Conservative Memristive Hyperchaotic System with Transient Quasi-Periodic Characteristics and Encryption Application
by Fei Yu, Bohong Tan, Ting He, Shaoqi He, Yuanyuan Huang, Shuo Cai and Hairong Lin
Mathematics 2025, 13(5), 726; https://doi.org/10.3390/math13050726 - 24 Feb 2025
Viewed by 111
Abstract
In comparison with dissipative chaos, conservative chaos is better equipped to handle the risks associated with the reconstruction of phase space due to the absence of attractors. This paper proposes a novel five-dimensional (5D) conservative memristive hyperchaotic system (CMHS), by incorporating memristors into [...] Read more.
In comparison with dissipative chaos, conservative chaos is better equipped to handle the risks associated with the reconstruction of phase space due to the absence of attractors. This paper proposes a novel five-dimensional (5D) conservative memristive hyperchaotic system (CMHS), by incorporating memristors into a four-dimensional (4D) conservative chaotic system (CCS). We conducted a comprehensive analysis, using Lyapunov exponent diagrams, bifurcation diagrams, phase portraits, equilibrium points, and spectral entropy maps to thoroughly verify the system’s chaotic and conservative properties. The system exhibited characteristics such as hyperchaos and multi-stability over an ultra-wide range of parameters and initial values, accompanied by transient quasi-periodic phenomena. Subsequently, the pseudorandom sequences generated by the new system were tested and demonstrated excellent performance, passing all the tests set by the National Institute of Standards and Technology (NIST). In the final stage of the research, an image-encryption application based on the 5D CMHS was proposed. Through security analysis, the feasibility and security of the image-encryption algorithm were confirmed. Full article
(This article belongs to the Section C2: Dynamical Systems)
Show Figures

Figure 1

Figure 1
<p>Lyapunov exponents for system (3): (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>6</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>2</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Bifurcation diagram for system (3): (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>6</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>2</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Phase diagram: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>−</mo> <mi>y</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>−</mo> <mi>z</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>−</mo> <mi>w</mi> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>−</mo> <mi>w</mi> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>−</mo> <mi>w</mi> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>−</mo> <mi>y</mi> <mo>−</mo> <mi>z</mi> </mrow> </semantics></math>.</p>
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<p>System (3): (<b>a</b>) LEs; (<b>b</b>) bifurcation diagram of <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>∈</mo> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1000</mn> <mo>]</mo> </mrow> </mrow> </semantics></math>; (<b>c</b>) time series diagram; (<b>d</b>–<b>f</b>) phase gram of <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>2.5</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>7.5</mn> <mo>,</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Bifurcation for parameter <span class="html-italic">b</span>: (<b>a</b>) Lyapunov exponential spectrum about <span class="html-italic">b</span>; (<b>b</b>) bifurcation diagram about <span class="html-italic">b</span>; (<b>c</b>) SE complexity about <span class="html-italic">b</span>.</p>
Full article ">Figure 6
<p>Bifurcation for parameter <span class="html-italic">a</span>: (<b>a</b>) Lyapunov exponential spectrum about <span class="html-italic">a</span>; (<b>b</b>) bifurcation diagram about <span class="html-italic">a</span>; (<b>c</b>) SE complexity about <span class="html-italic">a</span>.</p>
Full article ">Figure 7
<p>Bifurcation for parameter <span class="html-italic">k</span>: (<b>a</b>) Lyapunov exponential spectrum about <span class="html-italic">k</span>; (<b>b</b>) bifurcation diagram about <span class="html-italic">k</span>; (<b>c</b>) SE complexity about <span class="html-italic">k</span>.</p>
Full article ">Figure 8
<p>System (3) of <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>: (<b>a</b>) phase diagram of <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>; (<b>b</b>) time series of <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>500</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>c</b>) phase diagram of <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>260</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>The non-overlapping template <span class="html-italic">p</span>-value histogram.</p>
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<p>Image-encryption and -decryption algorithm flowchart.</p>
Full article ">Figure 11
<p>Baboon’s (<b>a</b>) original image, (<b>b</b>) encrypted image, (<b>c</b>) decrypted image. House’s (<b>f</b>) original image, (<b>g</b>) encrypted image, (<b>h</b>) decrypted image. Men’s (<b>k</b>) original image, (<b>l</b>) encrypted image, (<b>m</b>) decrypted image. Histogram of the original image: (<b>d</b>) Baboon, (<b>i</b>) House, (<b>n</b>) Men. Histogram of encrypted images: (<b>e</b>) Baboon, (<b>j</b>) House, (<b>o</b>) Men.</p>
Full article ">Figure 12
<p>Scatter diagrams of adjacent pixel correlation of House image. Original image correlation diagram in (<b>a</b>) horizontal direction, (<b>b</b>) vertical direction, (<b>c</b>) angle direction. Cipher image correlation diagram in (<b>d</b>) horizontal direction, (<b>e</b>) vertical direction, (<b>f</b>) angle direction.</p>
Full article ">Figure 13
<p>Noise attacks and data-loss attacks on Baboon image: (<b>a</b>,<b>d</b>,<b>g</b>) Baboon’s original image, (<b>b</b>) salt = 0.005, (<b>c</b>) salt-decrypted image, (<b>e</b>) center-cropping attack, (<b>f</b>) center-cropping attack decrypted image, (<b>h</b>) periphery-cropping attack, (<b>i</b>) periphery-cropping attack decrypted image.</p>
Full article ">
22 pages, 26819 KiB  
Article
A New Chaotic Color Image Encryption Algorithm Based on Memristor Model and Random Hybrid Transforms
by Yexia Yao, Xuemei Xu and Zhaohui Jiang
Appl. Sci. 2025, 15(2), 913; https://doi.org/10.3390/app15020913 - 17 Jan 2025
Viewed by 385
Abstract
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the [...] Read more.
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the chaotic system is proposed. To enhance encryption security, the pixel values of the image are preprocessed, and a hash function is used to generate a hash value, which is then incorporated into the secret keys generation process. Existing algorithms typically encrypt the three channels of a color image separately or perform encryption only at the pixel level, resulting in certain limitations in encryption effectiveness. To address this, this paper proposes a novel encryption algorithm based on 2D hyperchaotic maps that extends from single-channel encryption to multi-channel encryption (SEME-TDHM). The SEME-TDHM algorithm combines single-channel and multi-channel random scrambling, followed by local cross-diffusion of pixel values across different planes. By integrating both pixel-level and bit-level diffusion, the randomness of the image information distribution is significantly increased. Finally, the diffusion matrix is decomposed and restored to generate the encrypted color image. Simulation results and comparative analyses demonstrate that the SEME-TDHM algorithm outperforms existing algorithms in terms of encryption effectiveness. The encrypted image maintains a stable information entropy around 7.999, with average NPCR and UACI values close to the ideal benchmarks of 99.6169% and 33.4623%, respectively, further affirming its outstanding encryption effectiveness. Additionally, the histogram of the encrypted image shows a uniform distribution, and the correlation coefficient is nearly zero. These findings indicate that the SEME-TDHM algorithm successfully encrypts color images, providing strong security and practical utility. Full article
(This article belongs to the Special Issue Signal and Image Processing: From Theory to Applications)
Show Figures

Figure 1

Figure 1
<p>System framework diagram.</p>
Full article ">Figure 2
<p>A new system.</p>
Full article ">Figure 3
<p>Phase diagrams for different maps.</p>
Full article ">Figure 4
<p>Bifurcation diagram.</p>
Full article ">Figure 5
<p>Lyapunov exponents spectrum.</p>
Full article ">Figure 6
<p>Permutation entropy and its comparison chart.</p>
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<p>0–1 test result.</p>
Full article ">Figure 8
<p>Diagram of the encryption process.</p>
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<p>Global scrambling.</p>
Full article ">Figure 10
<p>Encrypted and decrypted images, original images are (<b>a1</b>) Candy, 256 × 256, (<b>a2</b>) Baboon, 512 × 512, (<b>a3</b>) House, 614 × 409, (<b>a4</b>) Bird, 768 × 512, (<b>a5</b>) Pepper, 1000 × 1000, (<b>a6</b>) Aircraft, 1024 × 1024; its corresponding encrypted images are (<b>b1</b>–<b>b6</b>); its corresponding decrypted images are (<b>c1</b>–<b>c6</b>).</p>
Full article ">Figure 11
<p>Decryption with different keys.</p>
Full article ">Figure 12
<p>Histogram analysis of images and cipher images: Red represents the R channel; green represents the G channel; blue represents the B channel.</p>
Full article ">Figure 12 Cont.
<p>Histogram analysis of images and cipher images: Red represents the R channel; green represents the G channel; blue represents the B channel.</p>
Full article ">Figure 13
<p>Three-channel adjacent pixel correlation of images and cipher images: red represents the distribution charts in horizontal direction; green represents the distribution charts in diagonal direction; blue represents the distribution charts in vertical direction.</p>
Full article ">Figure 14
<p>Noise test results of Candy, House and Pepper, Gaussian noise of (<b>a1</b>–<b>a3</b>) 1 × 10<sup>−7</sup>, (<b>b1</b>–<b>b3</b>) 1 × 10<sup>−6</sup>, Pepper and Salt noise of (<b>c1</b>–<b>c3</b>) 0.001, (<b>d1</b>–<b>d3</b>) 0.01.</p>
Full article ">Figure 14 Cont.
<p>Noise test results of Candy, House and Pepper, Gaussian noise of (<b>a1</b>–<b>a3</b>) 1 × 10<sup>−7</sup>, (<b>b1</b>–<b>b3</b>) 1 × 10<sup>−6</sup>, Pepper and Salt noise of (<b>c1</b>–<b>c3</b>) 0.001, (<b>d1</b>–<b>d3</b>) 0.01.</p>
Full article ">Figure 15
<p>Data loss analysis.</p>
Full article ">
20 pages, 6800 KiB  
Article
Dynamical Investigation of a Modified Cubic Map with a Discrete Memristor Using Microcontrollers
by Lazaros Laskaridis, Christos Volos, Aggelos Emmanouil Giakoumis, Efthymia Meletlidou and Ioannis Stouboulos
Electronics 2025, 14(2), 311; https://doi.org/10.3390/electronics14020311 - 14 Jan 2025
Viewed by 428
Abstract
This study presents a novel approach by implementing an active memristor in a hyperchaotic discrete system, based on a cubic map, which is implemented by using two different microcontrollers. The key contributions of this work are threefold. The use of two microcontrollers with [...] Read more.
This study presents a novel approach by implementing an active memristor in a hyperchaotic discrete system, based on a cubic map, which is implemented by using two different microcontrollers. The key contributions of this work are threefold. The use of two microcontrollers with improved characteristics, such as speed and memory, for faster and more accurate computations significantly improves upon previous systems. Also, for the first time, an active memristor is used in a discrete-time system, which is implemented by using a microcontroller. Furthermore, the system is compared with two different types of microcontrollers regarding the execution time and the quality of the produced bifurcation diagrams. The proposed memristive cubic map uses computationally efficient polynomial functions, which are well suited to microcontroller-based systems, in contrast to more resource-intensive trigonometric and exponential functions. Bifurcation diagrams and a Lyapunov exponent analysis from simulating the system in Mathematica revealed hyperchaotic behavior, along with other significant dynamical phenomena, such as regular orbits, chaotic trajectories, and transitions to chaos through mechanisms like period doubling and crisis phenomena. Experimental verification confirmed the consistency of the results across microcontroller platforms, underscoring the practicality and potential applications of active memristor-based chaotic systems. Full article
(This article belongs to the Special Issue Modern Circuits and Systems Technologies (MOCAST 2024))
Show Figures

Figure 1

Figure 1
<p>Characteristic hysteresis loop for (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>0</mn> </msub> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.01</mn> <mi>C</mi> </mrow> </semantics></math>, <span class="html-italic">A</span> = 20 mA, and a variety of frequencies <math display="inline"><semantics> <mi>ω</mi> </semantics></math> and for (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>0</mn> </msub> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.01</mn> <mi>C</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mi>ω</mi> </semantics></math> = 0.06 rad/s, and different amplitudes <span class="html-italic">A</span>.</p>
Full article ">Figure 2
<p>Bifurcation diagrams of <span class="html-italic">x</span> versus the parameter <span class="html-italic">d</span> for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) the Lyapunov spectrum, respectively, with the initial conditions <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Focused region of <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> showing period doubling phenomena.</p>
Full article ">Figure 4
<p>(<b>a</b>) Continuation (red) and bifurcation (blue) diagram of variable <span class="html-italic">x</span> with parameter <span class="html-italic">d</span> and (<b>b</b>) the focused region showing the existence of coexisting attractors.</p>
Full article ">Figure 5
<p>(<b>a</b>) Period-6 attractor and (<b>b</b>) quasiperiodic behavior for <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>0.958</mn> </mrow> </semantics></math> and for <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) focused regions of quasiperiodic behavior.</p>
Full article ">Figure 5 Cont.
<p>(<b>a</b>) Period-6 attractor and (<b>b</b>) quasiperiodic behavior for <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>0.958</mn> </mrow> </semantics></math> and for <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) focused regions of quasiperiodic behavior.</p>
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<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math> with initial conditions <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>Bifurcation diagrams of <span class="html-italic">x</span> and the Lyapunov spectrum versus the parameter <span class="html-italic">k</span> for (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math>, (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>, and (<b>e</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> with the initial conditions <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.7</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> with the initial conditions <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Experimental setup of system (<a href="#FD4-electronics-14-00311" class="html-disp-formula">4</a>) with the (<b>a</b>) PIC32MZ2048EFH144 and (<b>b</b>) STM32H723ZG microcontrollers.</p>
Full article ">Figure 10
<p>Experimental bifurcation diagrams of <span class="html-italic">x</span> versus the parameter <span class="html-italic">d</span> for <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> from the (<b>a</b>) PIC32 and (<b>c</b>) STM32 microcontrollers. Focused regions are presented in (<b>b</b>) and (<b>d</b>), respectively.</p>
Full article ">Figure 11
<p>Experimental bifurcation diagrams of <span class="html-italic">x</span> versus the parameter <span class="html-italic">k</span> for <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> from the (<b>a</b>) PIC32 and (<b>c</b>) STM32 microcontrollers. Focused regions are presented in (<b>b</b>) and (<b>d</b>), respectively.</p>
Full article ">Figure 11 Cont.
<p>Experimental bifurcation diagrams of <span class="html-italic">x</span> versus the parameter <span class="html-italic">k</span> for <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> from the (<b>a</b>) PIC32 and (<b>c</b>) STM32 microcontrollers. Focused regions are presented in (<b>b</b>) and (<b>d</b>), respectively.</p>
Full article ">Figure 12
<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>. The (<b>a</b>,<b>c</b>) diagrams were produced using the STM32 microcontroller while the (<b>b</b>,<b>d</b>) diagrams were produced using the PIC32 one.</p>
Full article ">Figure 12 Cont.
<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>. The (<b>a</b>,<b>c</b>) diagrams were produced using the STM32 microcontroller while the (<b>b</b>,<b>d</b>) diagrams were produced using the PIC32 one.</p>
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<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math>. The (<b>a</b>,<b>c</b>) diagrams were produced using the STM32 microcontroller while (<b>b</b>,<b>d</b>) were produced using the PIC32 one.</p>
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<p>Diagrams of <span class="html-italic">y</span> versus <span class="html-italic">x</span> for (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.7</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1.8</mn> </mrow> </semantics></math>. The (<b>a</b>,<b>c</b>) diagrams were produced using the STM32 microcontroller while (<b>b</b>,<b>d</b>) were produced using the PIC32 one.</p>
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26 pages, 9163 KiB  
Article
A Novel Multi-Channel Image Encryption Algorithm Leveraging Pixel Reorganization and Hyperchaotic Maps
by Wei Feng, Jiaxin Yang, Xiangyu Zhao, Zhentao Qin, Jing Zhang, Zhengguo Zhu, Heping Wen and Kun Qian
Mathematics 2024, 12(24), 3917; https://doi.org/10.3390/math12243917 - 12 Dec 2024
Cited by 12 | Viewed by 768
Abstract
Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to [...] Read more.
Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to jointly generate chaotic sequences, ensuring a larger key space and better randomness. During the encryption process, we first convert input images into two fused matrices through pixel reorganization. Then, we apply two rounds of scrambling and diffusion operations, coupled with one round of substitution operations, to the high 4-bit matrix. For the low 4-bit matrix, we conduct one round of substitution and diffusion operations. Extensive experiments and comparisons demonstrate that MIEA-PRHM outperforms many recent encryption algorithms in various aspects, especially in encryption efficiency. Full article
(This article belongs to the Special Issue Chaos-Based Secure Communication and Cryptography, 2nd Edition)
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<p>State value distributions of 2D-SCPM and 2D-ECHM: the first two 3D bifurcation diagrams illustrate the distributions of <math display="inline"><semantics> <msub> <mi>x</mi> <mi>i</mi> </msub> </semantics></math> for two hyperchaotic maps, whereas the subsequent two 3D bifurcation diagrams depict the distributions of <math display="inline"><semantics> <msub> <mi>y</mi> <mi>i</mi> </msub> </semantics></math>.</p>
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<p>Encryption process of our proposed MIEA-PRHM.</p>
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<p>Simple example of row–column joint scrambling: The blue squares represent the pixels of the input pixel matrix, the red squares denote the pixels currently being scrambled, and the gray squares indicate the scrambled pixels.</p>
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<p>Simple example of vector-level dynamic rotational diffusion: The blue squares represent the pixels of the input pixel matrix, the red squares denote the pixel regions currently undergoing diffusion, and the gray squares signify the diffused pixels. The bold gray arrows indicate the diffusion direction at each stage.</p>
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<p>Simple example of dual-operation dynamic partition substitution: The pink squares represent the pixels of the input pixel matrix, the red squares denote the pixels currently undergoing substitution, and the gray squares signify the substituted pixels.</p>
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<p>Decryption process of our proposed MIEA-PRHM.</p>
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<p>Visual assessment results for MIEA-PRHM: in the <b>first row</b>, six original test images, denoted as 5.1.09, 5.2.09, boat.512, 4.1.07, avion, and beeflowr, are concurrently input; the <b>second row</b> presents the corresponding encrypted images obtained simultaneously; and the <b>final row</b> exhibits the respective decrypted images.</p>
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<p>Key sensitivity experimental results for MIEA-PRHM: (<b>a1</b>) test image 5.2.08; (<b>a2</b>) ciphertext of 5.2.08 obtained with <math display="inline"><semantics> <mover accent="true"> <mi mathvariant="bold">K</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>; (<b>b1</b>) ciphertext obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b2</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b3</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>α</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b4</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b5</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b6</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b7</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b8</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>c1</b>) difference between (<b>a2</b>) and (<b>b1</b>); (<b>c2</b>) difference between (<b>a2</b>) and (<b>b2</b>); (<b>c3</b>) difference between (<b>a2</b>) and (<b>b3</b>); (<b>c4</b>) difference between (<b>a2</b>) and (<b>b4</b>); (<b>c5</b>) difference between (<b>a2</b>) and (<b>b5</b>); (<b>c6</b>) difference between (<b>a2</b>) and (<b>b6</b>); (<b>c7</b>) difference between (<b>a2</b>) and (<b>b7</b>); (<b>c8</b>) difference between (<b>a2</b>) and (<b>b8</b>).</p>
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<p>Visual assessment of plaintext sensitivity for MIEA-PRHM: (<b>a1</b>) test image 2.1.05; (<b>b1</b>) the least significant pixel bit at (2,3) was modified; (<b>c1</b>) the least significant bit at (511,512) was modified; (<b>d1</b>) difference between (<b>a1</b>) and (<b>b1</b>); (<b>e1</b>) difference between (<b>a1</b>) and (<b>c1</b>); (<b>a2</b>) encrypted image of (<b>a1</b>); (<b>b2</b>) encrypted image of (<b>b1</b>); (<b>c2</b>) encrypted image of (<b>c1</b>); (<b>d2</b>) difference between (<b>a2</b>) and (<b>b2</b>); and (<b>e2</b>) difference between (<b>a2</b>) and (<b>c2</b>).</p>
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<p>Experimental results of pixel correlation assessment for MIEA-PRHM: in the <b>first column</b>, the test images 2.1.02 and 4.2.03, along with their encrypted images, are presented; the <b>second column</b> exhibits the 3D correlation analysis plots of the horizontal orientation for the images displayed in the <b>first column</b>; the <b>third column</b> showcases the correlation analysis plots of the vertical orientation; and the <b>final column</b> depicts the correlation analysis plots of the diagonal orientation.</p>
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<p>Experimental results of pixel distribution assessment for MIEA-PRHM: in the <b>first column</b>, the test images 4.2.06 and 4.2.07, along with their encrypted images, are presented; the <b>second column</b> exhibits the 3D pixel distribution plots of the red channels for the images displayed in the <b>first column</b>; the <b>third column</b> showcases the pixel distribution plots of green channels; and the <b>final column</b> depicts the pixel distribution plots of blue channels.</p>
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<p>Experimental results of MIEA-PRHM against noise attacks: the <b>first row</b> presents five contaminated ciphertext images, with salt-and-pepper noise intensities of 0.01, 0.02, 0.03, 0.04, and 0.05, respectively, added to each; the <b>second row</b> shows the corresponding decrypted images.</p>
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<p>Experimental results of MIEA-PRHM against data loss: The <b>first row</b> presents five ciphertext images with some pixels missing. The numbers of their missing pixels are <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>64</mn> <mo>×</mo> <mn>64</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>96</mn> <mo>×</mo> <mn>96</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>160</mn> <mo>×</mo> <mn>160</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, respectively. The <b>second row</b> shows the corresponding decrypted images.</p>
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29 pages, 47454 KiB  
Article
A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination
by Yaoqun Xu, Jiaoyang Liu, Zelong You and Tianqi Zhang
Mathematics 2024, 12(22), 3457; https://doi.org/10.3390/math12223457 - 5 Nov 2024
Viewed by 906
Abstract
The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small [...] Read more.
The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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<p>Ackley function.</p>
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<p>Styblinski−Tang function.</p>
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<p>The design path of 2D-AST hyperchaotic mapping.</p>
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<p>Mathematical construction of 2D-AST hyperchaotic mapping.</p>
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<p>Bifurcation diagrams, (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <mi>a</mi> <mo>;</mo> <mfenced separators="|"> <mrow> <mi mathvariant="bold">b</mi> </mrow> </mfenced> <msub> <mrow> <mo> </mo> <mi>x</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <mi>b</mi> </mrow> </semantics></math>.</p>
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<p>Phase space trajectory: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>,<b>b</b>): 2D-AST chaos map Lyapunov exponent diagram; (<b>c</b>,<b>d</b>): Lyapunov exponent comparison chart [<a href="#B13-mathematics-12-03457" class="html-bibr">13</a>,<a href="#B14-mathematics-12-03457" class="html-bibr">14</a>,<a href="#B15-mathematics-12-03457" class="html-bibr">15</a>,<a href="#B16-mathematics-12-03457" class="html-bibr">16</a>,<a href="#B17-mathematics-12-03457" class="html-bibr">17</a>,<a href="#B18-mathematics-12-03457" class="html-bibr">18</a>].</p>
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<p>Sample entropy [<a href="#B13-mathematics-12-03457" class="html-bibr">13</a>,<a href="#B14-mathematics-12-03457" class="html-bibr">14</a>,<a href="#B15-mathematics-12-03457" class="html-bibr">15</a>,<a href="#B16-mathematics-12-03457" class="html-bibr">16</a>,<a href="#B17-mathematics-12-03457" class="html-bibr">17</a>,<a href="#B18-mathematics-12-03457" class="html-bibr">18</a>].</p>
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<p>Permutation entropy [<a href="#B13-mathematics-12-03457" class="html-bibr">13</a>,<a href="#B14-mathematics-12-03457" class="html-bibr">14</a>,<a href="#B15-mathematics-12-03457" class="html-bibr">15</a>,<a href="#B16-mathematics-12-03457" class="html-bibr">16</a>,<a href="#B17-mathematics-12-03457" class="html-bibr">17</a>,<a href="#B18-mathematics-12-03457" class="html-bibr">18</a>].</p>
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<p>Cobweb Plot, (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Clock spreading algorithm.</p>
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<p>Exploded image of coffee picture.</p>
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<p>Genetic recombination algorithm.</p>
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<p>Image encryption process.</p>
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<p>Encryption and decryption comparison of test objects: (<b>a</b>) coffee (512 × 512 × 3); (<b>b</b>) airplane (512 × 512 × 3); (<b>c</b>) baboon (256 × 256 × 3).</p>
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<p>Encryption and decryption comparison of test objects: (<b>a</b>) coffee (512 × 512 × 3); (<b>b</b>) airplane (512 × 512 × 3); (<b>c</b>) baboon (256 × 256 × 3).</p>
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<p>Histogram of original image and encrypted image: (<b>a</b>) image; (<b>b</b>) red histogram of (<b>a</b>); (<b>c</b>) green histogram of (<b>a</b>); (<b>d</b>) blue histogram of (<b>a</b>).</p>
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<p>Correlation simulation results: (<b>a</b>) image; (<b>b</b>) red plane of (<b>a</b>); (<b>c</b>) green plane of (<b>a</b>); (<b>d</b>) blue plane of (<b>a</b>).</p>
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<p>Initial value sensitivity analysis: (<b>a</b>) x = 0.5; (<b>b</b>) y = 0.5; (<b>c</b>) a = 10; (<b>d</b>) b = 10.</p>
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<p>Gaussian noise: (<b>a</b>) 0.0005; (<b>b</b>) 0.005; (<b>c</b>) 0.01; (<b>d</b>) salt-and-pepper noise.</p>
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<p>Blocking attack. (<b>a</b>) Picture after channel loss; (<b>b</b>) Decrypted image after the attack.</p>
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<p>Blocking attack: (<b>a</b>) 6.25% shear; (<b>b</b>) 12.5% shear; (<b>c</b>) 25% shear; (<b>d</b>) 50% shear.</p>
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15 pages, 5331 KiB  
Article
Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map
by Shuangshuang Peng, Honghui Shi, Renwang Li, Qian Xiang, Shaoxuan Dai and Yilin Li
Symmetry 2024, 16(8), 990; https://doi.org/10.3390/sym16080990 - 5 Aug 2024
Cited by 1 | Viewed by 803
Abstract
The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, [...] Read more.
The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos. Full article
(This article belongs to the Section Mathematics)
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<p>Bifurcation diagram of the one−dimensional chaotic map amplifier: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Discrete charge-controlled memristor Simulink model.</p>
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<p>Characteristic curves of the discrete memristor: (<b>a</b>) volt−ampere characteristic curve; (<b>b</b>) the current and voltage sequence output via scope.</p>
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<p>Simulink model of the three−dimensional discrete memristor map (3).</p>
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<p>Simulink simulation phase diagram of the three−dimensional discrete memristor map: (<b>a</b>) (<span class="html-italic">h</span>, <span class="html-italic">μ</span>) = (0.2, 0.75); (<b>b</b>) <span class="html-italic">(h</span>, <span class="html-italic">μ</span>) = (0.2, 0.89).</p>
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<p>Stability distribution of the equilibrium point sets E in the <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>h</mi> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mfenced separators="|"> <mrow> <mi>η</mi> </mrow> </mfenced> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> parameter plane.</p>
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<p>For fixed <span class="html-italic">k</span> = 0.1, <span class="html-italic">h</span> = 0.5 and (<span class="html-italic">x</span><sub>0</sub>, <span class="html-italic">y</span><sub>0</sub>, z<sub>0</sub>) = (0.1, 0, −1), the largest Lyapunov exponent (<b>top</b>) and bifurcation diagram (<b>bottom</b>) of the three−dimensional discrete memristor map with parameter <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> (0, 1): (<b>a</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <mi>γ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>b</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <mi>γ</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>The phase plane plots of chaotic/hyperchaotic attractors generated by the new model in the <span class="html-italic">x</span>–<span class="html-italic">y</span> plane for six groups of parameters <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> </mrow> </semantics></math> with (<span class="html-italic">x</span><sub>0</sub>, <span class="html-italic">y</span><sub>0</sub>, <span class="html-italic">z</span><sub>0</sub>) = (0.1, 0, −1) and r = 2. (<b>a</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mn>0.75</mn> </mrow> </mfenced> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mn>0.89</mn> </mrow> </mfenced> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.2</mn> <mo>,</mo> <mo> </mo> <mn>0.91</mn> </mrow> </mfenced> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.05</mn> <mo>,</mo> <mo> </mo> <mn>0.91</mn> </mrow> </mfenced> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.5</mn> <mo>,</mo> <mo> </mo> <mn>0.66</mn> </mrow> </mfenced> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>h</mi> <mo>,</mo> <mo> </mo> <mo> </mo> <mi>μ</mi> </mrow> </mfenced> <mo>=</mo> <mfenced separators="|"> <mrow> <mn>0.37</mn> <mo>,</mo> <mo> </mo> <mn>0.66</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The basins of attraction in the <span class="html-italic">x</span><sub>0</sub>-<span class="html-italic">y</span><sub>0</sub> initial plane for fixed <span class="html-italic">μ</span> = 0.89 and <span class="html-italic">z</span><sub>0</sub> = − 1 with different coupling parameter values of <span class="html-italic">h</span>, demonstrating the parameter effects of the coupling memristor on the multistability. (<b>a</b>) <span class="html-italic">h</span> = 0.02; (<b>b</b>) <span class="html-italic">h</span> = 0.05; (<b>c</b>) <span class="html-italic">h</span> = 0.08 (<b>d</b>) <span class="html-italic">h</span> = 0.1; (<b>e</b>) <span class="html-italic">h</span> = 0.4; (<b>f</b>) <span class="html-italic">h</span> = 0.5.</p>
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<p>The basins of attraction in the <span class="html-italic">x</span><sub>0</sub>-<span class="html-italic">y</span><sub>0</sub> initial plane for fixed (<span class="html-italic">h</span>, <span class="html-italic">μ</span>) = (0.1, 0.89) with different values of memristor initial condition <span class="html-italic">z</span><sub>0</sub>, demonstrating the initial effects of the coupling memristor on the heterogeneous multistability. (<b>a</b>) <span class="html-italic">z</span><sub>0</sub> = 0; (<b>b</b>) <span class="html-italic">z</span><sub>0</sub> = 0.5; (<b>c</b>) <span class="html-italic">z</span><sub>0</sub> = 1; (<b>d</b>) <span class="html-italic">z</span><sub>0</sub> = 2; (<b>e</b>) <span class="html-italic">z</span><sub>0</sub> = 3; (<b>f</b>) <span class="html-italic">z</span><sub>0</sub> = 4.</p>
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20 pages, 2860 KiB  
Article
A Secure Image Encryption Scheme Based on a New Hyperchaotic System and 2D Compressed Sensing
by Muou Liu, Chongyang Ning and Congxu Zhu
Entropy 2024, 26(7), 603; https://doi.org/10.3390/e26070603 - 16 Jul 2024
Cited by 1 | Viewed by 1583
Abstract
In insecure communication environments where the communication bandwidth is limited, important image data must be compressed and encrypted for transmission. However, existing image compression and encryption algorithms suffer from poor image reconstruction quality and insufficient image encryption security. To address these problems, this [...] Read more.
In insecure communication environments where the communication bandwidth is limited, important image data must be compressed and encrypted for transmission. However, existing image compression and encryption algorithms suffer from poor image reconstruction quality and insufficient image encryption security. To address these problems, this paper proposes an image-compression and encryption scheme based on a newly designed hyperchaotic system and two-dimensional compressed sensing (2DCS) technique. In this paper, the chaotic performance of this hyperchaotic system is verified by bifurcation diagrams, Lyapunov diagrams, approximate entropy, and permutation entropy, which have certain advantages over the traditional 2D chaotic system. The new 2D chaotic system as a pseudo-random number generator can completely pass all the test items of NIST. Meanwhile, this paper improves on the existing 2D projected gradient (2DPG) algorithm, which improves the quality of image compression and reconstruction, and can effectively reduce the transmission pressure of image data confidential communication. In addition, a new image encryption algorithm is designed for the new 2D chaotic system, and the security of the algorithm is verified by experiments such as key space size analysis and encrypted image information entropy. Full article
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<p>The bifurcation diagram of system (1): (<b>a</b>) bifurcation diagram of different parameters a corresponding to variable x; (<b>b</b>) bifurcation diagram of different parameters a corresponding to variable y.</p>
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<p>Phase diagram of system (<a href="#FD1-entropy-26-00603" class="html-disp-formula">1</a>): (<b>a</b>) 2D attractor when <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0.2</mn> <mo>,</mo> <mn>0.3</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>; (<b>b</b>) the relationship between the three iterative sequences <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) the relationship between the three iterative sequences <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>n</mi> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) the relationship between the three iterative sequences <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>n</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Graphs of Lyapunov exponents for 3 different 2D chaotic systems: (<b>a</b>) plot of Lyapunov exponent for 2D-CSCM; (<b>b</b>) plot of Lyapunov exponent for 2D-CLII; (<b>c</b>) plot of Lyapunov exponent for the proposed map corresponding to parameter <span class="html-italic">a</span>; (<b>d</b>) plot of Lyapunov exponent for the proposed map corresponding to parameter <span class="html-italic">b</span>.</p>
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<p>Plot of correlation for system (1): (<b>a</b>) autocorrelation; (<b>b</b>) cross-correlation.</p>
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<p>Approximate entropy of two different chaotic systems: (<b>a</b>) approximate entropy of the 2D-CLII chaotic system; (<b>b</b>) approximate entropy of the 2D chaotic system proposed in this paper.</p>
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<p>Permutation entropy of two different chaotic systems: (<b>a</b>) permutation entropy of the 2D-CLII chaotic system; (<b>b</b>) permutation entropy of the 2D chaotic system proposed in this paper.</p>
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<p>Four classical test images and their compressed sampled and reconstructed images: (<b>a</b>) original image Lena, (<b>b</b>) compressed and encrypted image Lena, (<b>c</b>) reconstructed image Lena, (<b>d</b>) original image Barbara, (<b>e</b>) compressed and encrypted image Barbara, (<b>f</b>) reconstructed image Barbara, (<b>g</b>) original image peppers, (<b>h</b>) compressed and encrypted image peppers, (<b>i</b>) reconstructed image peppers, (<b>j</b>) original image cameraman, (<b>k</b>) compressed and encrypted image cameraman, and (<b>l</b>) reconstructed image cameraman.</p>
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<p>Histogram of pixel distribution of plaintext image and ciphertext image of two test images. (<b>a</b>) Plaintext image peppers. (<b>b</b>) Histogram of plaintext image peppers. (<b>c</b>) Ciphertext image peppers. (<b>d</b>) Histogram of ciphertext image peppers. (<b>e</b>) Plaintext image cameraman. (<b>f</b>) Histogram of plaintext image cameraman. (<b>g</b>) Ciphertext image cameraman. (<b>h</b>) Histogram of Ciphertext image cameraman.</p>
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18 pages, 28354 KiB  
Article
A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
by Yumin Dong, Rui Yan, Qiong Zhang and Xuesong Wu
Mathematics 2024, 12(12), 1859; https://doi.org/10.3390/math12121859 - 14 Jun 2024
Viewed by 857
Abstract
In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine [...] Read more.
In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). In order to improve the security of the watermark, we use Lorenz hyperchaotic mapping to encrypt the watermark image. We adaptively determine the embedding factor by calculating the Bhattacharyya distance between the cover image and the watermark image, and combine the Alpha blending technique to embed the watermark image into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. The experimental results show that the average PSNR of our scheme is 45.9382 dB, and the SSIM is 0.9986. Through a large number of experimental results and comparative analysis, it shows that the scheme has good imperceptibility and robustness, indicating that we have achieved a good balance between imperceptibility, robustness and embedding capacity. Full article
(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
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<p>Attractor projection of Lorenz hyperchaotic system. (<b>a</b>) x-y plane, (<b>b</b>) x-z plane, (<b>c</b>) x-w plane, (<b>d</b>) y-z plane, (<b>e</b>) y-w plane, (<b>f</b>) z-w plane.</p>
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<p>Lorenz chaotic map encryption effect, (<b>d</b>–<b>f</b>) respectively (<b>a</b>–<b>c</b>), corresponding to the histogram.</p>
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<p>Example of discrete wavelet transform: (<b>a</b>) original image; (<b>b</b>) first-order wavelet transform; and (<b>c</b>) second-order wavelet transform.</p>
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<p>Watermark embedding process.</p>
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<p>Watermark extraction process.</p>
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<p>Cover image and watermark image.</p>
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<p>Watermarked image and corresponding extracted watermark image.</p>
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<p>Histogram comparison between cover image and embedded watermark image.</p>
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<p>Attacked cover image.</p>
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<p>Watermark images extracted from different attacks.</p>
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23 pages, 36196 KiB  
Article
An n-Dimensional Chaotic Map with Application in Reversible Data Hiding for Medical Images
by Yuli Yang, Ruiyun Chang, Xiufang Feng, Peizhen Li, Yongle Chen and Hao Zhang
Entropy 2024, 26(3), 254; https://doi.org/10.3390/e26030254 - 13 Mar 2024
Cited by 2 | Viewed by 1602
Abstract
The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based [...] Read more.
The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based chaotic system (nD-CTBCS) with a chaotic coupling model is suggested in this study. To create chaotic maps of any desired dimension, nD-CTBCS can take advantage of already-existing 1D chaotic maps as seed chaotic maps. Three two-dimensional chaotic maps are provided as examples to illustrate the impact. The findings of the evaluation and experiments demonstrate that the newly created chaotic maps function better, have broader chaotic intervals, and display hyperchaotic behavior. To further demonstrate the practicability of nD-CTBCS, a reversible data hiding scheme is proposed for the secure communication of medical images. The experimental results show that the proposed method has higher security than the existing methods. Full article
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<p>Bifurcation diagrams of (<b>a</b>) logistic; (<b>c</b>) sine; (<b>e</b>) fraction; (<b>g</b>) ICMIC maps; LEs of (<b>b</b>) logistic; (<b>d</b>) sine; (<b>f</b>) fraction; (<b>h</b>) ICMIC maps.</p>
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<p>Bifurcation diagrams of (<b>a</b>) logistic; (<b>c</b>) sine; (<b>e</b>) fraction; (<b>g</b>) ICMIC maps; LEs of (<b>b</b>) logistic; (<b>d</b>) sine; (<b>f</b>) fraction; (<b>h</b>) ICMIC maps.</p>
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<p>2D trajectories for different 2D chaotic maps: (<b>a</b>) 2D-LSM; (<b>b</b>) 2D-SIM; (<b>c</b>) 2D-SFM; (<b>d</b>) 2D-LSCM; (<b>e</b>) 2D-LSMCL; (<b>f</b>) 2D-LACM.</p>
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<p>2D trajectories for different 2D chaotic maps: (<b>a</b>) 2D-LSM; (<b>b</b>) 2D-SIM; (<b>c</b>) 2D-SFM; (<b>d</b>) 2D-LSCM; (<b>e</b>) 2D-LSMCL; (<b>f</b>) 2D-LACM.</p>
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<p>Bifurcation diagram for different 2D chaotic maps: (<b>a</b>) 2D-LSM; (<b>b</b>) 2D-SIM; (<b>c</b>) 2D-SFM.</p>
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<p>Two LEs for different 2D chaotic maps: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-LSM; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-SIM; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-SFM; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-LSM; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-SIM; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>E</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> of 2D-SFM.</p>
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<p>MLE of different chaotic maps.</p>
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<p>PEs of different chaotic maps.</p>
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<p>The flow chart of the proposed data-hiding algorithm.</p>
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<p>Segmentation mask generation process.</p>
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<p>Reversible data-hiding results: (<b>a</b>) test1 image; (<b>b</b>) test2 image; (<b>c</b>) test3 image; (<b>d</b>) test4 image; (<b>e</b>) Mask of test1; (<b>f</b>) Mask of test2; (<b>g</b>) Mask of test3; (<b>h</b>) Mask of test4; (<b>i</b>) QR of test1; (<b>j</b>) QR of test2; (<b>k</b>) QR of test3; (<b>l</b>) QR of test4; (<b>m</b>) result of test1; (<b>n</b>) result of test2; (<b>o</b>) result of test3; (<b>p</b>) result of test4.</p>
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<p>Decoding result: (<b>a</b>) test1 image; (<b>b</b>) test2 image; (<b>c</b>) test3 image; (<b>d</b>) test4 image; (<b>e</b>) QR of test1; (<b>f</b>) QR of test2; (<b>g</b>) QR of test3; (<b>h</b>) QR of test4; (<b>i</b>) authentication image of test1; (<b>j</b>) authentication image of test2; (<b>k</b>) authentication image of test3; (<b>l</b>) authentication image of test4; (<b>m</b>) PCE of test1; (<b>n</b>) PCE of test2; (<b>o</b>) PCE of test3; (<b>p</b>) PCE of test4.</p>
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<p>Histogram analysis: (<b>a</b>) histogram of test1; (<b>b</b>) histogram of encoded test1; (<b>c</b>) histogram of test2; (<b>d</b>) histogram of encoded test2; (<b>e</b>) histogram of test3; (<b>f</b>) histogram of encoded test3; (<b>g</b>) histogram of test4; (<b>h</b>) histogram of encoded test4.</p>
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<p>Histogram analysis: (<b>a</b>) histogram of test1; (<b>b</b>) histogram of encoded test1; (<b>c</b>) histogram of test2; (<b>d</b>) histogram of encoded test2; (<b>e</b>) histogram of test3; (<b>f</b>) histogram of encoded test3; (<b>g</b>) histogram of test4; (<b>h</b>) histogram of encoded test4.</p>
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<p>Correlation analysis: (<b>a</b>) test1-HVPS; (<b>b</b>) encoded test1-HVPS; (<b>c</b>) test2-HVPS; (<b>d</b>) encoded test2-HVPS; (<b>e</b>) test3-HVPS; (<b>f</b>) encoded test3-HVPS; (<b>g</b>) test4-HVPS; (<b>h</b>) encoded test4-HVPS.</p>
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<p>Correlation analysis: (<b>a</b>) test1-HVPS; (<b>b</b>) encoded test1-HVPS; (<b>c</b>) test2-HVPS; (<b>d</b>) encoded test2-HVPS; (<b>e</b>) test3-HVPS; (<b>f</b>) encoded test3-HVPS; (<b>g</b>) test4-HVPS; (<b>h</b>) encoded test4-HVPS.</p>
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<p>Encoded pixel ratio.</p>
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18 pages, 7596 KiB  
Article
A Novel Image Encryption Algorithm Based on Compressive Sensing and a Two-Dimensional Linear Canonical Transform
by Yuan-Min Li, Mingjie Jiang, Deyun Wei and Yang Deng
Fractal Fract. 2024, 8(2), 92; https://doi.org/10.3390/fractalfract8020092 - 31 Jan 2024
Cited by 4 | Viewed by 1650
Abstract
In this paper, we propose a secure image encryption method using compressive sensing (CS) and a two-dimensional linear canonical transform (2D LCT). First, the SHA256 of the source image is used to generate encryption security keys. As a result, the suggested technique is [...] Read more.
In this paper, we propose a secure image encryption method using compressive sensing (CS) and a two-dimensional linear canonical transform (2D LCT). First, the SHA256 of the source image is used to generate encryption security keys. As a result, the suggested technique is able to resist selected plaintext attacks and is highly sensitive to plain images. CS simultaneously encrypts and compresses a plain image. Using a starting value correlated with the sum of the image pixels, the Mersenne Twister (MT) is used to control a measurement matrix in compressive sensing. Then, the scrambled image is permuted by Lorenz’s hyper-chaotic systems and encoded by chaotic and random phase masks in the 2D LCT domain. In this case, chaotic systems increase the output complexity, and the independent parameters of the 2D LCT expand the key space of the suggested technique. Ultimately, diffusion based on addition and modulus operations yields a cipher-text image. Simulations showed that this cryptosystem was able to withstand common attacks and had adequate cryptographic features. Full article
(This article belongs to the Special Issue Fractional Fourier Transform and Its Applications in Signal Analysis)
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<p>Lorenz’s hyper-chaotic attractor: (<b>a</b>) x-y plane; (<b>b</b>) x-z plane; (<b>c</b>) x-w plane; (<b>d</b>) y-z plane; (<b>e</b>) y-w plane; (<b>f</b>) x-w-z plane.</p>
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<p>A flowchart of the procedure for encryption and decoding.</p>
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<p>The results of encryption and decryption: (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>) Unencrypted images; (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) the corresponding encrypted images at CR <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) the decrypted images.</p>
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<p>Results of encryption and decryption for Zelda with various CRs: (<b>a</b>–<b>d</b>) the cypher pictures for CR values of <math display="inline"><semantics> <mrow> <mn>0.2</mn> <mo>,</mo> <mn>0.4</mn> <mo>,</mo> <mn>0.6</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>0.8</mn> </mrow> </semantics></math>, respectively; (<b>e</b>–<b>h</b>) the corresponding decrypted images.</p>
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<p>Histogram analysis: The histograms of the plaintext images are shown in (<b>a</b>–<b>d</b>); the ground histograms of the cipher-text images are shown in (<b>e</b>–<b>h</b>); the histograms of the decrypted images are shown in (<b>i</b>–<b>l</b>).</p>
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<p>Correlation coefficient analysis: (<b>a</b>–<b>c</b>) Einstein’s correlations from three directions; (<b>g</b>–<b>i</b>) the corresponding encrypted image correlations; (<b>d</b>–<b>f</b>) Boat’s correlations from three directions; (<b>j</b>–<b>l</b>) the corresponding encrypted image correlations.</p>
Full article ">Figure 6 Cont.
<p>Correlation coefficient analysis: (<b>a</b>–<b>c</b>) Einstein’s correlations from three directions; (<b>g</b>–<b>i</b>) the corresponding encrypted image correlations; (<b>d</b>–<b>f</b>) Boat’s correlations from three directions; (<b>j</b>–<b>l</b>) the corresponding encrypted image correlations.</p>
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<p>Cropping attack analysis: (<b>a</b>–<b>d</b>) are encrypted images with data loss in different size and position; (<b>e</b>–<b>h</b>) are corresponding decrypted images.</p>
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<p>Key sensitivity analysis. A decrypted image was obtained by slightly changing the keys: <math display="inline"><semantics> <mrow> <msubsup> <mi>x</mi> <mrow> <mn>01</mn> </mrow> <mo>′</mo> </msubsup> <mo>,</mo> <mspace width="4.pt"/> <msubsup> <mi mathvariant="normal">y</mi> <mrow> <mn>01</mn> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math> for permutation, <math display="inline"><semantics> <mrow> <msubsup> <mi>x</mi> <mrow> <mn>02</mn> </mrow> <mo>′</mo> </msubsup> <mo>,</mo> <mspace width="4.pt"/> <msubsup> <mi mathvariant="normal">y</mi> <mrow> <mn>02</mn> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math> for diffusion, and <math display="inline"><semantics> <msubsup> <mi>x</mi> <mn>0</mn> <mo>′</mo> </msubsup> </semantics></math>, <math display="inline"><semantics> <msub> <mi>a</mi> <mn>1</mn> </msub> </semantics></math> for CRPM and the 2D LCT.</p>
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15 pages, 7046 KiB  
Article
Fast Encryption Algorithm Based on Chaotic System and Cyclic Shift in Integer Wavelet Domain
by Yuan-Min Li, Yang Deng, Mingjie Jiang and Deyun Wei
Fractal Fract. 2024, 8(2), 75; https://doi.org/10.3390/fractalfract8020075 - 24 Jan 2024
Cited by 4 | Viewed by 1543
Abstract
This paper introduces a new fast image encryption scheme based on a chaotic system and cyclic shift in the integer wavelet domain. In order to increase the effectiveness and security of encryption, we propose a new diffusion scheme by using bidirectional diffusion and [...] Read more.
This paper introduces a new fast image encryption scheme based on a chaotic system and cyclic shift in the integer wavelet domain. In order to increase the effectiveness and security of encryption, we propose a new diffusion scheme by using bidirectional diffusion and cyclic shift and apply it to our encryption scheme. First, a two-level integer wavelet transform is used to split the plaintext picture into four low-frequency components. Second, we use random sequences generated by Chen’s hyper-chaotic system to scramble four low-frequency components. The initial value is determined by Secure Hash Algorithm 256-bit (SHA256) and user-defined parameters, which increases the plaintext sensitivity. Then, the new diffusion scheme is applied to the matrix containing most of the information and matrices are transformed by a one-level inverse integer wavelet. Finally, to create the ciphertext image, the diffused matrices are subjected to the one-level inverse integer wavelet transform. In the simulation part, we examine the suggested algorithm’s encryption impact. The findings demonstrate that the suggested technique has a sufficient key space and can successfully fend off common attacks. Full article
(This article belongs to the Special Issue Recent Advances in Fractional Fourier Transforms and Applications)
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<p>Two-level Wavelet Decomposition.</p>
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<p>Chen hyper-chaotic attractor. (<b>a</b>) (r–s); (<b>b</b>) (r–t); (<b>c</b>) (r–u); (<b>d</b>) (s–t); (<b>e</b>) (s–u); (<b>f</b>) (t–u); (<b>g</b>) (r–s–t); (<b>h</b>) (r–s–u).</p>
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<p>The process of encryption.</p>
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<p>Encryption and decryption simulation results. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>,<b>m</b>,<b>p</b>) Plaintext image; (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>,<b>n</b>,<b>q</b>) Ciphertext image; (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>,<b>o</b>,<b>r</b>) Decrypt image.</p>
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<p>Encryption and decryption simulation results. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>,<b>m</b>,<b>p</b>) Plaintext image; (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>,<b>n</b>,<b>q</b>) Ciphertext image; (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>,<b>o</b>,<b>r</b>) Decrypt image.</p>
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<p>Histogram analysis. Plaintext image: (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>); Ciphertext image: (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>).</p>
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<p>Analysis of correlation coefficients. The Einstein correlations from the three directions are shown in (<b>a</b>–<b>c</b>), respectively. The equivalent Einstein correlations following encryption are (<b>g</b>–<b>i</b>). (<b>d</b>–<b>f</b>) are the correlations of Boat from three directions respectively. (<b>j</b>–<b>l</b>) are the corresponding correlations of Boat after encryption.</p>
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<p>Key sensitivity analysis. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>x</mi> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> <mi>y</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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22 pages, 636 KiB  
Article
A Novel Dynamic S-Box Generation Scheme Based on Quantum Random Walks Controlled by a Hyper-Chaotic Map
by Lijun Zhang, Caochuan Ma, Yuxiang Zhao and Wenbo Zhao
Mathematics 2024, 12(1), 84; https://doi.org/10.3390/math12010084 - 26 Dec 2023
Cited by 4 | Viewed by 1118
Abstract
For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for [...] Read more.
For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems. Full article
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<p>Trajectory diagram of map (<a href="#FD2-mathematics-12-00084" class="html-disp-formula">2</a>) with different values of the <math display="inline"><semantics> <msub> <mi>k</mi> <mn>22</mn> </msub> </semantics></math> control parameter (<math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>22</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> in subfigure (1), <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>22</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.9</mn> </mrow> </semantics></math> in subfigure (2), and <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>22</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.16</mn> </mrow> </semantics></math> in subfigure (3)).</p>
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<p>Lyapunov exponent and Bifurcation diagram of Sys (<a href="#FD2-mathematics-12-00084" class="html-disp-formula">2</a>) with different values of the <math display="inline"><semantics> <msub> <mi>k</mi> <mn>22</mn> </msub> </semantics></math> control parameter (Lyapunov exponents are shown in subfigure (1,2) and Bifurcation diagrams are shown in subfigure (3,4)).</p>
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<p>The Scale index of the Henon map, the logistic map, and the compound chaotic system.</p>
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<p><math display="inline"><semantics> <msub> <mi>H</mi> <mi>s</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mi>j</mi> </msub> </semantics></math> for the proposed PRNG.</p>
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<p>The function and basic principle of an <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>×</mo> <mi>n</mi> </mrow> </semantics></math> S-Box.</p>
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31 pages, 8215 KiB  
Article
Exploiting Newly Designed Fractional-Order 3D Lorenz Chaotic System and 2D Discrete Polynomial Hyper-Chaotic Map for High-Performance Multi-Image Encryption
by Wei Feng, Quanwen Wang, Hui Liu, Yu Ren, Junhao Zhang, Shubo Zhang, Kun Qian and Heping Wen
Fractal Fract. 2023, 7(12), 887; https://doi.org/10.3390/fractalfract7120887 - 16 Dec 2023
Cited by 73 | Viewed by 2534
Abstract
Chaos-based image encryption has become a prominent area of research in recent years. In comparison to ordinary chaotic systems, fractional-order chaotic systems tend to have a greater number of control parameters and more complex dynamical characteristics. Thus, an increasing number of researchers are [...] Read more.
Chaos-based image encryption has become a prominent area of research in recent years. In comparison to ordinary chaotic systems, fractional-order chaotic systems tend to have a greater number of control parameters and more complex dynamical characteristics. Thus, an increasing number of researchers are introducing fractional-order chaotic systems to enhance the security of chaos-based image encryption. However, their suggested algorithms still suffer from some security, practicality, and efficiency problems. To address these problems, we first constructed a new fractional-order 3D Lorenz chaotic system and a 2D sinusoidally constrained polynomial hyper-chaotic map (2D-SCPM). Then, we elaborately developed a multi-image encryption algorithm based on the new fractional-order 3D Lorenz chaotic system and 2D-SCPM (MIEA-FCSM). The introduction of the fractional-order 3D Lorenz chaotic system with the fourth parameter not only enables MIEA-FCSM to have a significantly large key space but also enhances its overall security. Compared with recent alternatives, the structure of 2D-SCPM is simpler and more conducive to application implementation. In our proposed MIEA-FCSM, multi-channel fusion initially reduces the number of pixels to one-sixth of the original. Next, after two rounds of plaintext-related chaotic random substitution, dynamic diffusion, and fast scrambling, the fused 2D pixel matrix is eventually encrypted into the ciphertext one. According to numerous experiments and analyses, MIEA-FCSM obtained excellent scores for key space (2541), correlation coefficients (<0.004), information entropy (7.9994), NPCR (99.6098%), and UACI (33.4659%). Significantly, MIEA-FCSM also attained an average encryption rate as high as 168.5608 Mbps. Due to the superiority of the new fractional-order chaotic system, 2D-SCPM, and targeted designs, MIEA-FCSM outperforms many recently reported leading image encryption algorithms. Full article
(This article belongs to the Topic Advances in Nonlinear Dynamics: Methods and Applications)
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<p>Phase trajectories of the fractional-order 3D Lorenz system with different initial states while the parameters <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>σ</mi> <mo>,</mo> <mi>ρ</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>α</mi> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mn>10</mn> <mo>,</mo> <mn>28</mn> <mo>,</mo> <mn>8</mn> <mo>/</mo> <mn>3</mn> <mo>,</mo> <mn>0.995</mn> <mo>}</mo> </mrow> </semantics></math>: (<b>a</b>) <span class="html-italic">x</span>-<span class="html-italic">y</span> plane; (<b>b</b>) <span class="html-italic">x</span>-<span class="html-italic">z</span> plane; (<b>c</b>) <span class="html-italic">y</span>-<span class="html-italic">z</span> plane; (<b>d</b>) 3D plot.</p>
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<p>Lyapunov exponent spectrums of the fractional-order 3D Lorenz system: (<b>a</b>) Sweep parameter <math display="inline"><semantics> <mi>σ</mi> </semantics></math> while <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>ρ</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>α</mi> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mn>28</mn> <mo>,</mo> <mn>8</mn> <mo>/</mo> <mn>3</mn> <mo>,</mo> <mn>0.99</mn> <mo>}</mo> </mrow> </semantics></math>; (<b>b</b>) sweep parameter <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> while <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>σ</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>α</mi> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mn>10</mn> <mo>,</mo> <mn>8</mn> <mo>/</mo> <mn>3</mn> <mo>,</mo> <mn>0.99</mn> <mo>}</mo> </mrow> </semantics></math>; (<b>c</b>) sweep parameter <math display="inline"><semantics> <mi>β</mi> </semantics></math> while <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>σ</mi> <mo>,</mo> <mi>ρ</mi> <mo>,</mo> <mi>α</mi> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mn>10</mn> <mo>,</mo> <mn>28</mn> <mo>,</mo> <mn>0.99</mn> <mo>}</mo> </mrow> </semantics></math>; (<b>d</b>) sweep parameter <math display="inline"><semantics> <mi>α</mi> </semantics></math> while <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>σ</mi> <mo>,</mo> <mi>ρ</mi> <mo>,</mo> <mi>β</mi> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mn>10</mn> <mo>,</mo> <mn>28</mn> <mo>,</mo> <mn>8</mn> <mo>/</mo> <mn>3</mn> <mo>}</mo> </mrow> </semantics></math>.</p>
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<p>3D LE presentations for 2D-SCPM.</p>
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<p>LE experiment results for 2D-SCPM and other five maps: the first column is the LE<math display="inline"><semantics> <msub> <mrow/> <mn>1</mn> </msub> </semantics></math> values of six maps; the second column is the LE<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> values.</p>
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<p>Bifurcation diagrams for three different maps: (<b>a1</b>,<b>a2</b>) are bifurcation diagrams for LASM; (<b>b1</b>,<b>b2</b>) are two diagrams for FOCM (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mi>v</mi> <mo>=</mo> <mn>0.789</mn> </mrow> </semantics></math>); (<b>c1</b>–<b>c4</b>) are four diagrams for 2D-SCPM.</p>
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<p>KE experiment results for 2D-SCPM and other five maps: the first column is the KE<math display="inline"><semantics> <msub> <mrow/> <mi>x</mi> </msub> </semantics></math> values of six maps; the second column is the KE<math display="inline"><semantics> <msub> <mrow/> <mi>y</mi> </msub> </semantics></math> values.</p>
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<p>Encryption process of MIEA-FCSM.</p>
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<p>Decryption process of MIEA-FCSM.</p>
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<p>Visual effect experiment results for MIEA-FCSM: (<b>a</b>,<b>d</b>), six grayscale images and two color images; (<b>b</b>,<b>e</b>), corresponding encrypted images; (<b>c</b>,<b>f</b>), corresponding decrypted images.</p>
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<p>Visual presentation of key sensitivity for MIEA-FCSM: (<b>a1</b>) 4.1.07; (<b>a2</b>) ciphertext of 4.1.07; (<b>b1</b>) cihpertext obtained after <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b2</b>) <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b3</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>z</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <msub> <mover accent="true"> <mi>z</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b4</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>σ</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>σ</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b5</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ρ</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>ρ</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b6</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b7</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>α</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b8</b>) <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b9</b>) <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b10</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b11</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>b</mi> <mo stretchy="false">˜</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>c1</b>) difference between (<b>b1</b>) and (<b>a2</b>); (<b>c2</b>) between (<b>b2</b>) and (<b>a2</b>); (<b>c3</b>) between (<b>b3</b>) and (<b>a2</b>); (<b>c4</b>) between (<b>b4</b>) and (<b>a2</b>); (<b>c5</b>) between (<b>b5</b>) and (<b>a2</b>); (<b>c6</b>) between (<b>b6</b>) and (<b>a2</b>); (<b>c7</b>) between (<b>b7</b>) and (<b>a2</b>); (<b>c8</b>) between (<b>b8</b>) and (<b>a2</b>); (<b>c9</b>) between (<b>b9</b>) and (<b>a2</b>); (<b>c10</b>) between (<b>b10</b>) and (<b>a2</b>); (<b>c11</b>) between (<b>b11</b>) and (<b>a2</b>).</p>
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<p>Visual presentation of plaintext sensitivity for MIEA-FCSM: (<b>a1</b>) 2.1.06; (<b>a2</b>) the lowest bit of the first pixel on the red channel is negated; (<b>a3</b>) the lowest bit of the last pixel on the blue channel is negated; (<b>b1</b>) difference between (<b>a1</b>) and (<b>a2</b>); (<b>b2</b>) difference between (<b>a1</b>) and (<b>a3</b>); (<b>c1</b>) ciphertext of (<b>a1</b>); (<b>c2</b>) ciphertext of (<b>a2</b>); (<b>c3</b>) ciphertext of (<b>a3</b>); (<b>d1</b>) difference between (<b>c1</b>) and (<b>c2</b>); (<b>d2</b>) difference between (<b>c1</b>) and (<b>c3</b>).</p>
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<p>Pixel distribution representations for MIEA-FCSM: (<b>a1</b>) 2.1.01; (<b>b1</b>,<b>c1</b>,<b>d1</b>) are pixel distribution diagrams for the red, green, and blue channels of (<b>a1</b>); (<b>a2</b>) ciphertext of (<b>a1</b>); (<b>b2</b>,<b>c2</b>,<b>d2</b>) are three pixel distribution diagrams for (<b>a2</b>); (<b>a3</b>) 2.1.07; (<b>b3</b>,<b>c3</b>,<b>d3</b>) are three pixel distribution diagrams for (<b>a3</b>); (<b>a4</b>) ciphertext of (<b>a3</b>); (<b>b4</b>,<b>c4</b>,<b>d4</b>) are three pixel distribution diagrams for (<b>a4</b>).</p>
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<p>Adjacent pixel correlation representations: (<b>a1</b>) 2.1.05; (<b>b1</b>) correlation analysis diagram for (<b>a1</b>) in the horizontal direction; (<b>c1</b>) diagram for (<b>a1</b>) in the vertical direction; (<b>d1</b>) diagram for (<b>a1</b>) in the diagonal direction; (<b>a2</b>) ciphertext of 2.1.05; (<b>b2</b>,<b>c2</b>,<b>d2</b>) are correlation analysis diagrams for (<b>a2</b>); (<b>a3</b>) 2.1.06; (<b>b3</b>,<b>c3</b>,<b>d3</b>) are correlation analysis diagrams for (<b>a3</b>); (<b>a4</b>) ciphertext of 2.1.06; (<b>b4</b>,<b>c4</b>,<b>d4</b>) are correlation analysis diagrams for (<b>a2</b>).</p>
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<p>Robustness analysis for MIEA-FCSM: (<b>a1</b>–<b>a4</b>) are encrypted images contaminated by varying intensities of noise; (<b>b1</b>–<b>b4</b>) are decrypted images of (<b>a1</b>–<b>a4</b>); (<b>c1</b>–<b>c4</b>) are encrypted images with 25% missing pixels at different positions; (<b>d1</b>–<b>d4</b>) are decrypted images of (<b>c1</b>–<b>c4</b>).</p>
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25 pages, 6529 KiB  
Article
Image Encryption Using Quantum 3D Mobius Scrambling and 3D Hyper-Chaotic Henon Map
by Ling Wang, Qiwen Ran and Junrong Ding
Entropy 2023, 25(12), 1629; https://doi.org/10.3390/e25121629 - 6 Dec 2023
Viewed by 1383
Abstract
In encryption technology, image scrambling is a common processing operation. This paper proposes a quantum version of the 3D Mobius scrambling transform based on the QRCI model, which changes not only the position of pixels but also the gray values. The corresponding quantum [...] Read more.
In encryption technology, image scrambling is a common processing operation. This paper proposes a quantum version of the 3D Mobius scrambling transform based on the QRCI model, which changes not only the position of pixels but also the gray values. The corresponding quantum circuits are devised. Furthermore, an encryption scheme combining the quantum 3D Mobius transform with the 3D hyper-chaotic Henon map is suggested to protect the security of image information. To facilitate subsequent processing, the RGB color image is first represented with QRCI. Then, to achieve the pixel-level permutation effect, the quantum 3D Mobius transform is applied to scramble bit-planes and pixel positions. Ultimately, to increase the diffusion effect, the scrambled image is XORed with a key image created by the 3D hyper-chaotic Henon map to produce the encrypted image. Numerical simulations and result analyses indicate that our designed encryption scheme is secure and reliable. It offers better performance in the aspect of key space, histogram variance, and correlation coefficient than some of the latest algorithms. Full article
(This article belongs to the Special Issue Advanced Technology in Quantum Cryptography)
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Figure 1

Figure 1
<p>Basic quantum modules: (<b>a</b>) adder, (<b>b</b>) subtractor, (<b>c</b>) double-output adder, (<b>d</b>) multiplier, (<b>e</b>) comparator.</p>
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<p>The bifurcation diagram of <span class="html-italic">u</span>-sequence.</p>
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<p>The phase diagram of 3D hyper-chaotic Henon map.</p>
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<p>Elementary quantum circuits: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mi>X</mi> </msub> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mi>Y</mi> </msub> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mi>L</mi> </msub> </semantics></math>.</p>
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<p>Quantum circuit for 3D Mobius scrambling transform.</p>
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<p>Elementary quantum circuits: (<b>a</b>) <math display="inline"><semantics> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <msubsup> <mi>S</mi> <mrow> <mi>Y</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <msubsup> <mi>S</mi> <mrow> <mi>X</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </semantics></math>.</p>
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<p>Quantum circuit for inverse 3D Mobius transform.</p>
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<p>Scrambling results: (<b>a</b>) Peppers, (<b>b</b>) scrambled Peppers.</p>
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<p>Histograms: (<b>a</b>) R channel of Peppers. (<b>b</b>) G channel of Peppers. (<b>c</b>) B channel of Peppers. (<b>d</b>) R channel of scrambled Peppers. (<b>e</b>) G channel of scrambled Peppers. (<b>f</b>) B channel of scrambled Peppers.</p>
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<p>Scrambling results of Arnold/Fibonacci transform: (<b>a</b>) Arnold. (<b>b</b>) Fibonacci.</p>
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<p>Histograms of using Arnold/Fibonacci transform: (<b>a</b>) R channel. (<b>b</b>) G channel. (<b>c</b>) B channel.</p>
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<p>The encryption procedure.</p>
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<p>Quantum circuit for implementing one iteration.</p>
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<p>The complete quantum circuit for 3D hyper-chaotic Henon map.</p>
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<p>Quantum circuit for synchronizing positions and bit-planes.</p>
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<p>Quantum circuit to implement XOR operation.</p>
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<p>Simulation results.</p>
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<p>Histograms of Lena.</p>
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<p>Histograms of Baboon.</p>
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<p>The correlation distributions.</p>
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<p>The spectrum distributions.</p>
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<p>Decrypted images with incorrect keys: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>18</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>w</mi> <mn>0</mn> </msub> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>12</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>f</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>u</mi> </mrow> <mn>0</mn> <mo>′</mo> </msubsup> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>g</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>v</mi> </mrow> <mn>0</mn> <mo>′</mo> </msubsup> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>. (<b>h</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>w</mi> </mrow> <mn>0</mn> <mo>′</mo> </msubsup> <mo>+</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>13</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Decrypted images with various noise intensities: (<b>a</b>) 0.25. (<b>b</b>) 0.5. (<b>c</b>) 0.75. (<b>d</b>) 1.</p>
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<p>The cutting attack results.</p>
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25 pages, 15235 KiB  
Article
A Robust Memristor-Enhanced Polynomial Hyper-Chaotic Map and Its Multi-Channel Image Encryption Application
by Kun Qian, Yang Xiao, Yinjie Wei, Di Liu, Quanwen Wang and Wei Feng
Micromachines 2023, 14(11), 2090; https://doi.org/10.3390/mi14112090 - 12 Nov 2023
Cited by 14 | Viewed by 1450
Abstract
Nowadays, the utilization of memristors to enhance the dynamical properties of chaotic systems has become a popular research topic. In this paper, we present the design of a novel 2D memristor-enhanced polynomial hyper-chaotic map (2D-MPHM) by utilizing the cross-coupling of two TiO2 [...] Read more.
Nowadays, the utilization of memristors to enhance the dynamical properties of chaotic systems has become a popular research topic. In this paper, we present the design of a novel 2D memristor-enhanced polynomial hyper-chaotic map (2D-MPHM) by utilizing the cross-coupling of two TiO2 memristors. The dynamical properties of the 2D-MPHM were investigated using Lyapunov exponents, bifurcation diagrams, and trajectory diagrams. Additionally, Kolmogorov entropy and sample entropy were also employed to evaluate the complexity of the 2D-MPHM. Numerical analysis has demonstrated the superiority of the 2D-MPHM. Subsequently, the proposed 2D-MPHM was applied to a multi-channel image encryption algorithm (MIEA-MPHM) whose excellent security was demonstrated by key space, key sensitivity, plaintext sensitivity, information entropy, pixel distribution, correlation analysis, and robustness analysis. Finally, the encryption efficiency of the MIEA-MPHM was evaluated via numerous encryption efficiency tests. These tests demonstrate that the MIEA-MPHM not only possesses excellent security but also offers significant efficiency advantages, boasting an average encryption rate of up to 87.2798 Mbps. Full article
(This article belongs to the Special Issue Advanced Technologies in Memristor Devices)
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Figure 1
<p>Pinched hysteresis loops of adopted memristor model.</p>
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<p>Coupling schematic diagram for 2D-MPHM.</p>
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<p>LE diagram of 2D-MPHM.</p>
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<p>Six bifurcation diagrams for 2D-MPHM: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>; (<b>c</b>,<b>d</b>) 3D bifurcation diagrams.</p>
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<p>Six trajectory diagrams for 2D-MPHM: the first row is the trajectory diagram plotted when <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>a</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>8</mn> <mo>)</mo> </mrow> </semantics></math>; the second row is trajectory diagram plotted when <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>; the third row is trajectory diagram plotted when <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>SE comparison for 2D-MPHM and four other newly proposed 2D maps.</p>
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<p>KE comparison for 2D-MPHM and four other newly proposed 2D maps.</p>
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<p>Encryption process of MIEA-MPHM.</p>
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<p>Visual effects of encryption and decryption: (<b>a</b>,<b>d</b>): six grayscale images and two color images; (<b>b</b>,<b>e</b>) encrypted ones generated by MIEA-MPHM; (<b>c</b>,<b>f</b>) decrypted ones generated by MIEA-MPHM.</p>
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<p>Key sensitivity test results for MIEA-MPHM: (<b>a</b>) 4.2.05 and 4.2.06; (<b>b</b>) ciphertext images generated with <math display="inline"><semantics> <mrow> <msup> <mi>γ</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>γ</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) generated with <math display="inline"><semantics> <mrow> <msubsup> <mi>x</mi> <mn>0</mn> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi>y</mi> <mn>0</mn> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msup> <mi>a</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>a</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msup> <mi>b</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>b</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>g</b>) original ciphertext images of 4.2.05 and 4.2.06; (<b>h</b>) differences between (<b>b</b>,<b>g</b>); (<b>i</b>) differences between (<b>c</b>,<b>g</b>); (<b>j</b>) differences between (<b>d</b>,<b>g</b>); (<b>k</b>) differences between (<b>e</b>,<b>g</b>); (<b>l</b>) differences between (<b>f</b>,<b>g</b>).</p>
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<p>Plaintext sensitivity test results for MIEA-MPHM: (<b>a</b>) the first set of input images 4.1.06 and 4.1.08; (<b>b</b>) one pixel bit located at (1,1,1) in 4.1.06 was changed; (<b>c</b>) difference between (<b>a</b>,<b>b</b>); (<b>d</b>) ciphertext of (<b>a</b>); (<b>e</b>) ciphertext of (<b>b</b>); (<b>f</b>) difference between (<b>d</b>,<b>e</b>); (<b>g</b>) the second set of input images 4.1.04 and 4.1.05; (<b>h</b>) one pixel bit located at (256,256,3) in 4.1.05 was changed; (<b>i</b>) difference between (<b>g</b>,<b>h</b>); (<b>j</b>) ciphertext of (<b>g</b>); (<b>k</b>) ciphertext of (<b>h</b>); (<b>l</b>) difference between (<b>j</b>,<b>k</b>).</p>
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<p>NPCR test results of MIEA-MPHM and four state-of-the-art algorithms [<a href="#B48-micromachines-14-02090" class="html-bibr">48</a>,<a href="#B49-micromachines-14-02090" class="html-bibr">49</a>,<a href="#B50-micromachines-14-02090" class="html-bibr">50</a>,<a href="#B51-micromachines-14-02090" class="html-bibr">51</a>].</p>
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<p>UACI test results of MIEA-MPHM and four state-of-the-art algorithms [<a href="#B48-micromachines-14-02090" class="html-bibr">48</a>,<a href="#B49-micromachines-14-02090" class="html-bibr">49</a>,<a href="#B50-micromachines-14-02090" class="html-bibr">50</a>,<a href="#B51-micromachines-14-02090" class="html-bibr">51</a>].</p>
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<p>Three-dimensional pixel distribution diagrams of input images and their ciphertext images: (<b>a</b>) beeflowr; (<b>b</b>–<b>d</b>) are three diagrams for R, G, and B channels of (<b>a</b>); (<b>e</b>) ciphertext of (<b>a</b>); (<b>f</b>–<b>h</b>) are diagrams for (<b>e</b>); (<b>i</b>) barnfall; (<b>j</b>–<b>l</b>) are diagrams for (<b>i</b>); (<b>m</b>) ciphertext of (<b>i</b>); (<b>n</b>–<b>p</b>) are diagrams for (<b>m</b>).</p>
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<p>Pixel correlation analysis diagrams of input images and their ciphertext counterparts: (<b>a</b>) 4.1.04; (<b>b</b>) is the diagram for (<b>a</b>) in horizontal direction; (<b>c</b>) is the diagram for (<b>a</b>) in vertical direction; (<b>d</b>) is the diagram for (<b>a</b>) in diagonal direction; (<b>e</b>) 4.1.05; (<b>f</b>–<b>h</b>) are three diagrams for (<b>e</b>) in three directions; (<b>i</b>) ciphertext of 4.1.04; (<b>j</b>–<b>l</b>) are three diagrams for (<b>i</b>); (<b>m</b>) ciphertext of 4.1.05; (<b>n</b>–<b>p</b>) are three diagrams for (<b>m</b>).</p>
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<p>Robustness analysis on noise for MIEA-MPHM: (<b>a</b>) ciphertext images with salt-and-pepper noise of intensity 0.02; (<b>b</b>) noise of intensity 0.04; (<b>c</b>) noise of intensity 0.06; (<b>d</b>) noise of intensity 0.08; (<b>e</b>) noise of intensity 0.10; (<b>f</b>–<b>j</b>) are decrypted images of (<b>a</b>–<b>e</b>).</p>
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<p>Robustness analysis on data loss for MIEA-MPHM: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>512</mn> <mo>×</mo> <mn>384</mn> </mrow> </semantics></math> pixels missing in red channel; (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>512</mn> <mo>×</mo> <mn>384</mn> </mrow> </semantics></math> pixels missing in blue channel; (<b>c</b>) <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> </mrow> </semantics></math> pixels missing in all channels; (<b>d</b>) <math display="inline"><semantics> <mrow> <mn>192</mn> <mo>×</mo> <mn>192</mn> <mo>×</mo> <mn>6</mn> </mrow> </semantics></math> pixels missing; (<b>e</b>) <math display="inline"><semantics> <mrow> <mn>224</mn> <mo>×</mo> <mn>224</mn> <mo>×</mo> <mn>6</mn> </mrow> </semantics></math> pixels missing; (<b>f</b>–<b>j</b>) are decrypted images of (<b>a</b>–<b>e</b>).</p>
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