Skip to main content
SpringerLink
Log in

A perceptual entanglement-based image authentication with tamper localisation

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

Abstract

With the recent development in image editing tools the content of digital images can be easily manipulated. Research efforts are being deployed to develop passive and active methods for image authentication. It has been deemed that passive methods are not effective and not yet mature enough to detect even common image manipulations. In contrast, active methods are more efficient, especially when it comes to exposing doctored images where the changes are perfectly performed. In this work, we propose an effective perceptual entanglement-based approach for image authentication. Firstly, the image spatial blocks undergo a local encoding with global entanglement using image spectral matting. Then, the reconstruction residual is utilized for image integrity verification through image disentanglement as an inverse matting process. In our experiments, we demonstrate how a broken spatial and intensity affinity in the image induces higher residual sensitivity, thereby revealing image manipulation. Additionally, our approach is capable of pinpointing the location of tampered pixels as an explanatory authentication decision.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Availability of data and materials: The datasets analysed during the current study are available from the links http://sipi.usc.edu/database, http://www.ux.uis.no/~tranden/brodatz.html, and https://github.com/ieee8023/covid-chestxray-dataset.

Notes

  1. http://sipi.usc.edu/database

  2. http://www.ux.uis.no/~tranden/brodatz.html

  3. https://github.com/ieee8023/covid-chestxray-dataset

References

  1. Zheng L, Zhang Y, Thing V (2018) A survey on image tampering and its detection in real-world photos. Vis Commun Image Represent 58:380–399

    Article  Google Scholar 

  2. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig. 1(20)

  3. Hu W, Chen W, Huang D (2016) Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha matte. Multimedia Tools and Applications 75:3495–2016

    Article  Google Scholar 

  4. Garg P, Kishore R (2020) Performance comparison of various watermarking techniques. Multimedia Tools Appl 79:25921–25967

    Article  Google Scholar 

  5. Asikuzzaman M, Pickering MR (2018) An overview of digital video watermarking. IEEE Trans Circuits Syst Video Technol 28(9):2131–2153. https://doi.org/10.1109/TCSVT.2017.2712162

    Article  Google Scholar 

  6. Nurul K, Kamsin A, Yee P, Hameedur R (2018) A Review of Text Watermarking: Theory. Methods Appl IEEE Access 6:1–20

    Google Scholar 

  7. Hua G, Huang J, Shi Y, Thing V (2016) Twenty years of digital audio watermarking - a comprehensive review. Signal Process 128:222–242

    Article  Google Scholar 

  8. Kumar C, Singh AK, Kumar PA (2018) Recent survey on image watermarking techniques and its application in e-governance. Multimedia Tools Appl 77:3597–3622

    Article  Google Scholar 

  9. Shih F (2017) Digital Watermarking and Steganography: Fundamentals and Techniques. Taylor and Francis Group CRC Press, Boston

    Book  Google Scholar 

  10. Sridhar P, Sathiya RR (2017) Crypto-watermarking for secure and robust transmission of multispectral images. In: Zaimis E (ed) International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), Melmaruvathur, India, pp 153–163. https://doi.org/10.1109/ICCPEIC.2017.8290357

  11. Lei B, Zhao X, Lei H, Ni D, Chen S, Zhou F, Wang T (2017) Multipurpose watermarking scheme via intelligent method and chaotic map. Multimedia Tools Appl 78:27085–27107

    Article  Google Scholar 

  12. Singh L, Singh AK, Singh PK (2020) Secure data hiding techniques: a survey. Multimedia Tools Appl 79:15901–15921

    Article  Google Scholar 

  13. Chandramouli R, Memon N (2001) Analysis of lsb based image steganography techniques. In: Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), vol 3. Thessaloniki, Greece, pp 1019–1022. https://doi.org/10.1109/ICIP.2001.958299

  14. Ping WW, Memon N (2001) Secret and public key image watermarking schemes for image authentication and ownership verification. IEEE Trans Image Process 10(10):1593–1601. https://doi.org/10.1109/83.951543

    Article  Google Scholar 

  15. Bender W, Gruhl D, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3.4):313–336. https://doi.org/10.1147/sj.353.0313

  16. Purna Kumari B, Subramanyam Rallabandi VP (2008) Modified patchwork-based watermarking scheme for satellite imagery. Signal Process 88(4):891–904. https://doi.org/10.1016/j.sigpro.2007.10.009

    Article  Google Scholar 

  17. Pickholtz R, Schilling D, Milstein L (1982) Theory of spread-spectrum communications - a tutorial. IEEE Trans Commun 30(5):855–884. https://doi.org/10.1109/TCOM.1982.1095533

    Article  Google Scholar 

  18. Samcovic A, Turan J (2007) Digital image watermarking by spread spectrum. In: Proceedings of the 11th Conference on 11th WSEAS International Conference on Communications - vol 11. ICCOM’07, pp 29–32. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA (2007)

  19. Chitra K, Venkatesan VP (2016) Spatial domain watermarking technique: An introspective study. In: Proceedings of the International Conference on Informatics and Analytics. ICIA-16, pp 1–6. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2980258.2980363

  20. Mun SM, Nam SH, Jang H, Lee HK (2017) A robust blind watermarking using convolutional neural network. arXiv:1704.03248

  21. Zhu J, Kaplan R, Johnson J, Fei-Fei L (2018) Hidden: Hiding data with deep networks. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018. Springer, Munich, Germany, pp 682–697

    Chapter  Google Scholar 

  22. Zhong X, Huang P-C, Mastorakis S, Shih FY (2021) An automated and robust image watermarking scheme based on deep neural networks. IEEE Trans Multimedia 23:1951–1961. https://doi.org/10.1109/TMM.2020.3006415

    Article  Google Scholar 

  23. Rafigh M, Moghaddam ME (2010) A robust evolutionary based digital image watermarking technique in dct domain. In: 2010 Seventh International Conference on Computer Graphics, Imaging and Visualization, Sydney, NSW, Australia, pp 105–109. https://doi.org/10.1109/CGIV.2010.24

  24. Vishwakarma VP, Sisaudia V (2018) Gray-scale image watermarking based on de-kelm in dct domain. In: Procedia Computer Science:International Conference on Computational Intelligence and Data Science, vol 132. Gurugram, India, pp 1012–1020. https://doi.org/10.1016/j.procs.2018.05.017

  25. Chang T, Pan I, Huang P (2019) A robust DCT-2DLDA watermark for color images. Multimedia Tools Appl 78:9169–9191. https://doi.org/10.1007/s11042-018-6505-4

    Article  Google Scholar 

  26. Patra JC, Phua JE, Bornand C (2010) A novel DCT domain CRT-based watermarking scheme for image authentication surviving JPEG compression. Digit Signal Proc 20(6):1597–1611. https://doi.org/10.1016/j.dsp.2010.03.010

    Article  Google Scholar 

  27. Tew Y, Wong K (2014) Information hiding in hevc standard using adaptive coding block size decision. In: 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, pp 5502–5506. https://doi.org/10.1109/ICIP.2014.7026113

  28. Bhatnagar G, Jonathan Wu QM, Raman B (2012) Robust gray-scale logo watermarking in wavelet domain. Computers & Electrical Engineering. 38(5), 1164–1176. https://doi.org/10.1016/j.compeleceng.2012.02.002. Special issue on Recent Advances in Security and Privacy in Distributed Communications and Image processing

  29. Lu W, Sun W, Lu H (2012) Novel robust image watermarking based on subsampling and dwt. Multimedia Tools Appl 60(1):31–46. https://doi.org/10.1007/s11042-011-0794-1

    Article  Google Scholar 

  30. Malik S, Kishore RR (2018) Fractional fourier transform and position shuffling based digital image watermarking scheme and its performance analysis. Int J Adv Stud Sci Res 4(1):1–7

    Google Scholar 

  31. Wang J, Du Z (2019) A method of processing color image watermarking based on the Haar wavelet. J Vis Commun Image Represent 64:102627. https://doi.org/10.1016/j.jvcir.2019.102627

    Article  Google Scholar 

  32. Kalra GS, Talwar R, Sadawarti H (2015) Adaptive digital image watermarking for color images in frequency domain. Multimedia Tools Appl 74(17):6849–6869. https://doi.org/10.1007/s11042-014-1932-3

    Article  Google Scholar 

  33. Hamidi M, Haziti ME, Cherifi H, Hassouni ME (2018) Hybrid Blind Robust Image Watermarking Technique Based on DFT-DCT and Arnold Transform. Multimedia Tools Appl 77(20):27181–27214. https://doi.org/10.1007/s11042-018-5913-9

    Article  Google Scholar 

  34. Kang X-B, Zhao F, Lin G-F, Chen YJ (2018) A novel hybrid of dct and svd in dwt domain for robust and invisible blind image watermarking with optimal embedding strength. Multimedia Tools Appl 77(11):13197–13224. https://doi.org/10.1007/s11042-017-4941-1

    Article  Google Scholar 

  35. Li D, Deng L, Bhooshan Gupta B, Wang H, Choi C (2019) A novel cnn based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci 479:432–447. https://doi.org/10.1016/j.ins.2018.02.060

    Article  Google Scholar 

  36. Zheng W, Mo S, Jin X, Qu Y, Deng F, Shuai J, Xie Z, Zheng C, Long S (2018) Robust and high capacity watermarking for image based on dwt-svd and cnn. In: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China, pp 1233–1237. https://doi.org/10.1109/ICIEA.2018.8397898

  37. Sengupta S, Jayaram V, Curless B, Seitz SM, Kemelmacher-Shlizerman I (2020) Background matting: The world is your green screen. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp 2288–2297. https://doi.org/10.1109/CVPR42600.2020.00236

  38. Su Q (2016) Color Image Watermarking: Algorithms and Technologies. De Gruyter, Berlin, Germany. https://doi.org/10.1515/9783110487732

  39. Levin A, Lischinski D, Weiss Y (2006) A closed form solution to natural image matting. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 1, pp 61–68. IEEE Computer Society, Los Alamitos, CA, USA. https://doi.org/10.1109/CVPR.2006.18

  40. Wang J, Cohen MF (2007) Simultaneous matting and compositing. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, pp 1–8. https://doi.org/10.1109/CVPR.2007.383079

  41. Wang J, Cohen MF (2008) Image and video matting: A survey. Foundations and Trends® in Computer Graphics and Vision. 3(2), 97–175. https://doi.org/10.1561/0600000019

  42. He K, Sun J, Tang X (2011) Single Image Haze Removal Using Dark Channel Prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353. https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  43. Dai S, Wu Y (2008) Motion from blur. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp 1–8. https://doi.org/10.1109/CVPR.2008.4587582

  44. Fan J, Shen X, Wu Y (2010) Closed-loop adaptation for robust tracking. In: Daniilidis K, Maragos P, Paragios N (eds) Computer Vision - ECCV 2010. Springer, Berlin, Heidelberg, pp 411–424

    Chapter  Google Scholar 

  45. Hsu E, Mertens T, Paris S, Avidan S, Durand F (2008) Light mixture estimation for spatially varying white balance. ACM Trans Graph 27(3):1–7. https://doi.org/10.1145/1360612.1360669

    Article  Google Scholar 

  46. Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242. https://doi.org/10.1109/TPAMI.2007.1177

    Article  Google Scholar 

  47. Xiao D, Deng M, Zhu X (2015) A reversible image authentication scheme based on compressive sensing. Multimedia Tools Appl 74(18):7729–7752. https://doi.org/10.1007/s11042-014-2017-z

    Article  Google Scholar 

  48. Ebert DS, Musgrave FK, Peachey D, Perlin K, Worley S (2002) Texturing and Modeling: A Procedural Approach, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

    Google Scholar 

  49. Ansari I, Pant M, Ahn CW (2016) SVD based fragile watermarking scheme for tamper localization and self-recovery. Int J Mach Learn Cybern 7. https://doi.org/10.1007/s13042-015-0455-1

  50. Chen T-H, Huang W-L, Lin Y (2010) Chaos-based image integrity authentication code. In: Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP ’10, pp 13–16. IEEE Computer Society, Darmstadt, Germany. https://doi.org/10.1109/IIHMSP.2010.11

Download references

Acknowledgements

We gratefully acknowledge the support of the Computer Research Institute of Montreal (CRIM), the Ministère de l’Économie et de l’Innovation (MEI) of Quebec, and The Natural Sciences and Engineering Research Council of Canada (NSERC).

Funding

The research leading to these results received funding from The Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant Agreement No RGPIN-2020-05171.

Author information

Authors and Affiliations

  1. Research Department, Computer Research Institute of Montreal, 405 Av. Ogilvy #101, Montreal, H3N 1M3, QC, Canada

    Mohamed Dahmane

Authors
  1. Mohamed Dahmane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Dahmane.

Ethics declarations

Ethical Approval and Consent to participate

Not Applicable

Conflict of interest/Competing interests

The authors have no relevant financial or non-financial interests to disclose.

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

Dahmane, M. A perceptual entanglement-based image authentication with tamper localisation. Multimed Tools Appl 83, 38193–38208 (2024). https://doi.org/10.1007/s11042-023-16791-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16791-y

Keywords

Search

Navigation