[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Image operator forensics and sequence estimation using robust deep neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Digital images can be manipulated with recent tools. Image forensics examines the image from several angles to spot any anomalies. Most techniques are applicable to detect a single operation on the image. In actual practice, fake photos are manipulated with multiple operations and compression algorithms. A convolutional neural network with a reasonable size is designed to detect operators and the respective sequences for two operators in particular. The bottleneck strategy is incorporated to optimize the network training cost and a high-depth network. The detection of a particular operator depends on inherent statistical information. A single global average pooling layer preserves the statistical information in a convolutional neural network. The strength of existing detection techniques is also reduced in low-resolution and high-compression environments. The proposed method performs better than existing techniques on compressed small-size images even though forensic is difficult in small-size and compressed images due to inadequate statistical traces. The proposed convolutional neural network also applies to detect operators with unknown specifications and compression not used in training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9.
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data sharing does not apply to this article as used datasets are publicly available.

References

  1. Yang P, Baracchi D, Ni R et al (2020) A survey of deep learning-based source image forensics. J Imaging 6:9. https://doi.org/10.3390/jimaging6030009

    Article  Google Scholar 

  2. Agarwal S, Jung K-H (2022) Median filtering forensics based on optimum thresholding for low-resolution compressed images. Multimed Tools Appl 81:7047–7062. https://doi.org/10.1007/s11042-022-11945-w

    Article  Google Scholar 

  3. Peng L, Liao X, Chen M (2021) Resampling parameter estimation via dual-filtering based convolutional neural network. Multimed Syst 27:363–370. https://doi.org/10.1007/s00530-020-00697-y

    Article  Google Scholar 

  4. Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model. In: IH and MMSec 2014 - Proceedings of the 2014 ACM Information Hiding and Multimedia Security Workshop. ACM Press, New York, New York, USA, pp 165–170

  5. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7:868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  6. Shi YQ, Sutthiwan P, Chen L (2013) Textural features for steganalysis. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). pp 63–77

  7. Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. ACM, New York, NY, USA, pp 5–10

  8. Bayar B, Stamm MC (2018) Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2018.2825953

  9. Li H, Luo W, Qiu X, Huang J (2018) Identification of various image operations using residual-based features. IEEE Trans Circuits Syst Video Technol 28:31–45. https://doi.org/10.1109/TCSVT.2016.2599849

    Article  Google Scholar 

  10. Boroumand M, Fridrich J (2018) Deep learning for detecting processing history of images. Electron Imaging 30:213-1-213–9. https://doi.org/10.2352/ISSN.2470-1173.2018.07.MWSF-213

  11. Mazumdar A, Singh J, Tomar YS, Bora PK (2018) Universal image manipulation detection using deep siamese convolutional neural network. arXiv Prepr arXiv180806323

  12. Mazumdar A, Singh J, Tomar YS, Bora PK (2019) Detection of image manipulations using siamese convolutional neural networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 226–233

  13. Xue H, Liu H, Li J, et al (2020) Sed-net: detecting multi-type edits of images. In: 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1–6

  14. Chen Y, Kang X, Shi YQ, Wang ZJ (2019) A multi-purpose image forensic method using densely connected convolutional neural networks. J Real-Time Image Process 16:725–740. https://doi.org/10.1007/s11554-019-00866-x

    Article  Google Scholar 

  15. Singhal D, Gupta A, Tripathi A, Kothari R (2020) CNN-based multiple manipulation detector using frequency domain features of image residuals. ACM Trans Intell Syst Technol 11:1–26. https://doi.org/10.1145/3388634

    Article  Google Scholar 

  16. Chen J, Liao X, Wang W et al (2023) SNIS: A signal noise separation-based network for post-processed image forgery detection. IEEE Trans Circuits Syst Video Technol 33:935–951. https://doi.org/10.1109/TCSVT.2022.3204753

    Article  Google Scholar 

  17. Stamm MC, Chu X, Liu KJR (2013) Forensically determining the order of signal processing operations. In: Proceedings of the 2013 IEEE International Workshop on Information Forensics and Security, WIFS 2013

  18. Chu X, Chen Y, Liu KJR (2016) Detectability of the order of operations: An information theoretic approach. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2015.2510958

  19. Comesaña P (2012) Detection and information theoretic measures for quantifying the distinguishability between multimedia operator chains. In: WIFS 2012 - Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security

  20. Bayar B, Stamm MC (2018) Towards order of processing operations detection in jpeg-compressed images with convolutional neural networks. Electron Imaging 2018:211-1-211–9. https://doi.org/10.2352/ISSN.2470-1173.2018.07.MWSF-211

  21. Chen J, Liao X, Qin Z (2021) Identifying tampering operations in image operator chains based on decision fusion. Signal Process Image Commun 95:116287. https://doi.org/10.1016/j.image.2021.116287

    Article  Google Scholar 

  22. Liao X, Li K, Zhu X, Liu KJR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Signal Process 14:955–968. https://doi.org/10.1109/JSTSP.2020.3002391

    Article  Google Scholar 

  23. Liu Q, Chen Z (2015) Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Trans Intell Syst Technol 5:1–30. https://doi.org/10.1145/2560365

    Article  Google Scholar 

  24. Barni M, Costanzo A, Nowroozi E, Tondi B (2018) Cnn-based detection of generic contrast adjustment with JPEG post-processing. In: Proceedings - International Conference on Image Processing, ICIP. IEEE, pp 3803–3807

  25. Cheng X, Kadry S, Meqdad MN, Crespo RG (2022) CNN supported framework for automatic extraction and evaluation of dermoscopy images. J Supercomput 78:17114–17131. https://doi.org/10.1007/s11227-022-04561-w

    Article  Google Scholar 

  26. Sharma N, Gupta S, Mehta P et al (2022) Offline signature verification using deep neural network with application to computer vision. J Electron Imaging 31:41210. https://doi.org/10.1117/1.JEI.31.4.041210

    Article  Google Scholar 

  27. Abdulrahman H, Chaumont M, Montesinos P, Magnier B (2016) Color image steganalysis based on steerable gaussian filters bank. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security - IH&MMSec ’16. ACM Press, New York, New York, USA, pp 109–114

  28. Schaefer G, Stich M (2003) UCID: an uncompressed color image database. In: Yeung MM, Lienhart RW, Li C-S (eds) Storage and Retrieval Methods and Applications for Multimedia 2004. pp 472–480

  29. Bas P, Filler T, Pevný T (2011) Break our steganographic system: The ins and outs of organizing boss. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 59–70

  30. Xu G, Wu H-Z, Shi Y-Q (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23:708–712. https://doi.org/10.1109/LSP.2016.2548421

    Article  Google Scholar 

  31. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd Int Conf Mach Learn ICML 2015 1:448–456

  32. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: ICML 2010 - Proceedings, 27th International Conference on Machine Learning

  33. Xu G (2017) Deep convolutional neural network to detect J-UNIWARD. In: IH and MMSec 2017 - Proceedings of the 2017 ACM Workshop on Information Hiding and Multimedia Security

  34. Yedroudj M, Comby F, Chaumont M (2018) Yedrouj-Net: An efficient CNN for spatial steganalysis. 2018 IEEE Int Conf Acoust Speech Signal Process 2018-April:2092–2096. https://doi.org/10.1109/ICASSP.2018.8461438

  35. Lin M, Chen Q, Yan S (2014) Network in network. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings

  36. Xavier Glorot YB (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. pp 249-256

Download references

Acknowledgements

This research was supported by Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049788).

Author information

Authors and Affiliations

  1. Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India

    Saurabh Agarwal

  2. Department of Software Convergence, Andong National University, Gyeongbuk, 36729, Republic of Korea

    Saurabh Agarwal & Ki-Hyun Jung

Authors
  1. Saurabh Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ki-Hyun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ki-Hyun Jung.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, S., Jung, KH. Image operator forensics and sequence estimation using robust deep neural network. Multimed Tools Appl 83, 47431–47454 (2024). https://doi.org/10.1007/s11042-023-17389-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17389-0

Keywords

Search

Navigation