Abstract
Local Binary Pattern (LBP) has been widely used for texture analysis, feature extraction, visual investigation, pattern matching, and image authentication. It is essential to investigate the effectiveness of LBP, for tamper detection, tamper localization, and ownership identification of a watermarked image, which are highly desirable in many human-centric applications like health-care, military communication, remote sensing, and law enforcement. In this article, a Reversible Watermarking Technique has been introduced to verify image integrity, authenticity and error correction using LBP and Hamming codes. The LBP values have been calculated from (\(2 \times 2\)) original pixel block of the cover image. Then the watermark is inserted within the Least Significant Bit of the interpolated pixels. Here, LBP operator is used to solve image authentication and tamper detection problem whereas Hamming code is used to detect and correct the error in the extraction phase. Some standard NIST recommended steganalysis have been performed to evaluate the robustness and imperceptibility. It is observed that the proposed scheme is secure and robust against various attacks. It can also detect tampered locations and can verify the ownership of an image. Experimental results are compared with the existing watermarking schemes to demonstrate the superiority of the proposed scheme. It also shows good perceptible quality with a high payload and less computational cost.
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Algaet, M. A., Noh, Z. A. B. M., Basari, A. S. B. H., Shibghatullah, A. S., Milad, A. A., Abugharsa, A. B., & Mustapha, A. (2017). Development of robust medical image transmission via Wi-Fi IEEE 802.11 b in the hospital area. Wireless Personal Communications, 95(2), 1617–1634.
Sharma, A., Singh, A. K., & Ghrera, S. P. (2017). Robust and secure multiple watermarking for medical images. Wireless Personal Communications, 92(4), 1611–1624.
Alias Sathya, S. P., & Ramakrishnan, S. (2018). Fibonacci based key frame selection and scrambling for video watermarking in DWT-SVD domain. Wireless Personal Communications, 102(2), 2011–2031.
Tsai, J. L., Lo, N. W., & Wu, T. C. (2013). A new password-based multi-server authentication scheme robust to password guessing attacks. Wireless Personal Communications, 71(3), 1977–1988.
Banu, N. M., & Sujatha, S. (2015). Improved tampering detection for image authentication based on image partitioning. Wireless Personal Communications, 84(1), 69–85.
Pal, P., Chowdhuri, P., & Jana, B. (2018). Weighted matrix based reversible watermarking scheme using color image. Multimedia Tools and Applications, 77, 23073–23098. https://doi.org/10.1007/s11042-017-5568-y.
Thirunavukkarasu, V., Kumar, J. S., Chae, G. S., & Kishorkumar, J. (2018). Non-intrusive forensic detection method using DSWT with reduced feature set for copy-move image tampering. Wireless Personal Communications, 98(4), 3039–3057.
Fridrich, J., Goljan, M., & Du, R. (2001). Invertible authentication. In Security and watermarking of multimedia contents III (Vol. 4314, pp. 197–209). International Society for Optics and Photonics.
Phiasai, T., Temdee, P., & Chamnongthai, K. (2015). An anti-cropping watermarking method for facial images using prediction and Weber ratio techniques. Wireless Personal Communications, 85(2), 421–448.
Noor, R., Khan, A., & Sarfaraz, A. (2019). High performance and energy efficient image watermarking for video using a mobile device. Wireless Personal Communications, 104(4), 1535–1551.
Zhang, H., Wang, C., & Zhou, X. (2017). An improved secure semi-fragile watermarking based on LBP and Arnold transform. Journal of Information Processing Systems, 13(5), 1382–1396.
Su, Q., Niu, Y., Wang, Q., & Sheng, G. (2013). A blind color image watermarking based on DC component in the spatial domain. Optik-International Journal for Light and Electron Optics, 124(23), 6255–6260.
Verma, V. S., Jha, R. K., & Ojha, A. (2015). Significant region based robust watermarking scheme in lifting wavelet transform domain. Expert Systems with Applications, 42(21), 8184–8197.
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Wenyin, Z., & Shih, F. Y. (2011). Semi-fragile spatial watermarking based on local binary pattern operators. Optics Communications, 284(16–17), 3904–3912.
Roy, S. K., Chanda, B., Chaudhuri, B. B., Banerjee, S., Ghosh, D. K., & Dubey, S. R. (2018). Local directional ZigZag pattern: A rotation invariant descriptor for texture classification. Pattern Recognition Letters, 108, 23–30.
Wei, X., Wang, H., Guo, G., & Wan, H. (2018). Multiplex image representation for enhanced recognition. International Journal of Machine Learning and Cybernetics, 9(3), 383–392.
Chang, J. D., Chen, B. H., & Tsai, C. S. (2013). LBP-based fragile watermarking scheme for image tamper detection and recovery. In 2013 international symposium on next-generation electronics (pp. 173–176). IEEE.
Ding, F., Zhu, G., & Shi, Y. Q. (2013). A novel method for detecting image sharpening based on local binary pattern. In International workshop on digital watermarking (pp. 180–191). Springer, Berlin, Heidelberg.
Pinjari, S. A., & Patil, N. N. (2016). A pixel based fragile watermarking technique using LBP (Local Binary Pattern). In 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC) (pp. 194–196). IEEE.
Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., & Mathkour, H. (2017). Passive detection of image forgery using DCT and local binary pattern. Signal, Image and Video Processing, 11(1), 81–88.
Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4), 857–867.
Mishra, P., Mishra, N., Sharma, S., & Patel, R. (2013). Region duplication forgery detection technique based on SURF and HAC. The Scientific World Journal, 2013, 267691.
Uliyan, D. M., Jalab, H. A., & Wahab, A. W. A. (2015). Copy move image forgery detection using Hessian and center symmetric local binary pattern. In 2015 IEEE Conference on Open Systems (ICOS) (pp. 7–11). IEEE.
Jung, K. H., & Yoo, K. Y. (2009). Data hiding method using image interpolation. Computer Standards & Interfaces, 31(2), 465–470.
Lee, C. F., & Huang, Y. L. (2012). An efficient image interpolation increasing payload in reversible data hiding. Expert Systems with Applications, 39(8), 6712–6719.
Hu, J., & Li, T. (2015). Reversible steganography using extended image interpolation technique. Computers & Electrical Engineering, 46, 447–455.
Parah, S. A., Sheikh, J. A., Loan, N. A., & Bhat, G. M. (2017). A robust and computationally efficient digital watermarking technique using inter block pixel differencing. In Multimedia forensics and security (pp. 223–252). Springer, Cham.
Jana, B. (2016). High payload reversible data hiding scheme using weighted matrix. Optik-International Journal for Light and Electron Optics, 127(6), 3347–3358.
Jana, B., Giri, D., & Mondal, S. K. (2018). Dual image based reversible data hiding scheme using (7, 4) Hamming code. Multimedia Tools and Applications, 77(1), 763–785.
Nguyen, T. D., & Le, H. D. (2021). A reversible data hiding scheme based on (5, 3) Hamming code using extra information on overlapped pixel blocks of grayscale images. Multimedia Tools and Applications, 80, 13099–13120.
University of California, San Diego. STARE Image Database. Retrieved May 2, 2018 from https://cecas.clemson.edu/~ahoover/stare/.
Nottingham Trent University, UK. UCID Image Database. Retrieved May 2, 2018 from http://jasoncantarella.com/downloads/ucid.v2.tar.gz.
University of Southern California. The USC-SIPI Image Database. Retrieved May 2, 2018 from http://sipi.usc.edu/database/database.php.
Funt et al. (2017). HDR Dataset Computational Vision Lab Computing Science, Simon Fraser University, Burnaby, BC, Canada. Retrieved May 2, 2017 from http://www.cs.sfu.ca/~colour/data/funt_hdr/.
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE international conference on computer vision (p. 416). IEEE.
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Pal, P., Jana, B. & Bhaumik, J. An Image Authentication and Tampered Detection Scheme Exploiting Local Binary Pattern Along with Hamming Error Correcting Code. Wireless Pers Commun 121, 939–961 (2021). https://doi.org/10.1007/s11277-021-08666-y
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DOI: https://doi.org/10.1007/s11277-021-08666-y