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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 32523–32563 | Cite as

Fragile watermarking for image tamper detection and localization with effective recovery capability using K-means clustering

  • Assem AbdelhakimEmail author
  • Hassan I. Saleh
  • Mai Abdelhakim
Article
  • 52 Downloads

Abstract

The continuous development of forgery attacks on critical multimedia applications necessitates having accurate image tamper detection and localization techniques. In this paper, an image authentication approach is designed using a block-based fragile watermarking. Moreover, an effective recovery technique based on unsupervised machine learning is proposed. The authentication data is generated, for each 8 × 8 image block, using the Discrete Cosine Transform. A block dependency is established, for authenticating an image block, through using part of the authentication data of a distant block. Such block-dependency provides more accurate tamper detection and enables precise localization of tampered regions. At the recovery phase, a block is divided into smaller sub-blocks of size 2 × 2, where the recovery data is calculated through the K-means clustering. A fragile watermarking, in the spatial domain, is employed for embedding the watermark that is generated from the authentication and recovery data. We examine the effectiveness of the proposed approach under some of the most common attacks of image tampering, including copy move, constant average, and vector quantization attacks. Our approach is compared with several existing methods. Experimental results show that the proposed technique provides superior tampering detection and localization performance, and is capable of recovering the tampered regions more effectively.

Keywords

Image authentication Tampering localization Fragile watermarking Discrete Cosine Transform Unsupervised machine learning K-means clustering 

Notes

References

  1. 1.
    Benrhouma O, Hermassi H, El-Latif AAA, Belghith S (2016) Chaotic watermark for blind forgery detection in images. Multimed Tools Appl 75(14):8695–8718CrossRefGoogle Scholar
  2. 2.
    Bohra A, Farooq O (2009) Blind self-authentication of images for robust watermarking using integer wavelet transform. AEU-International Journal of Electronics and Communications 63(8):703–707CrossRefGoogle Scholar
  3. 3.
    Cao F, An B, Wang J, Ye D, Wang H (2017) Hierarchical recovery for tampered images based on watermark self-embedding. Displays 46:52–60CrossRefGoogle Scholar
  4. 4.
    Celik MU, Sharma G, Saber E, Tekalp AM (2002) Hierarchical watermarking for secure image authentication with localization. IEEE Trans Image Process 11(6):585–595CrossRefGoogle Scholar
  5. 5.
    Chang C-C, Fan Y-H, Tai W-L (2008) Four-scanning attack on hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recogn 41(2):654–661CrossRefGoogle Scholar
  6. 6.
    De Vleeschouwer C, Delaigle J-F, Macq B (2002) Invisibility and application functionalities in perceptual watermarking an overview. Proc IEEE 90(1):64–77CrossRefGoogle Scholar
  7. 7.
    Di Martino F, Sessa S (2012) Fragile watermarking tamper detection with images compressed by fuzzy transform. Inf Sci 195:62–90CrossRefGoogle Scholar
  8. 8.
    El'arbi M, Amar CB (2014) Image authentication algorithm with recovery capabilities based on neural networks in the DCT domain. IET Image Process 8(11):619–626CrossRefGoogle Scholar
  9. 9.
    Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25CrossRefGoogle Scholar
  10. 10.
    Gareth J (2010) An introduction to statistical learning: with applications in R. SpringerGoogle Scholar
  11. 11.
    Ghosal S, Mandal J (2014) Binomial transform based fragile watermarking for image authentication. Journal of Information Security and Applications 19(4–5):272–281CrossRefGoogle Scholar
  12. 12.
    Hasan Y, Hassan A (2004) Fragile blockwise image authentication thwarting vector quantization attack. Paper presented at the Signal Processing and Information Technology, 2004. Proceedings of the Fourth IEEE International Symposium on,Google Scholar
  13. 13.
    He H, Chen F, Tai H-M, Kalker T, Zhang J (2012) Performance analysis of a block-neighborhood-based self-recovery fragile watermarking scheme. IEEE Transactions on Information Forensics and Security 7(1):185–196CrossRefGoogle Scholar
  14. 14.
    He H, Zhang J, Chen F (2007) Block-wise fragile watermarking scheme based on scramble encryption. Paper presented at the Bio-Inspired Computing: Theories and Applications, 2007. BIC-TA 2007. Second International Conference on,Google Scholar
  15. 15.
    Ho AT, Zhu X, Shen J, Marziliano P (2008) Fragile Watermarking Based on Encoding of the Zeroes of the $ z $-Transform. IEEE Transactions on Information Forensics and Security 3(3):567–569CrossRefGoogle Scholar
  16. 16.
    Hsu C-S, Tu S-F (2016) Image tamper detection and recovery using adaptive embedding rules. Measurement 88:287–296CrossRefGoogle Scholar
  17. 17.
    Huang P-W, Peng A-W, Lin P-L (2004) VQ Attack Resilient and Tamper Proofing Digital Watermark for Image Authentication and RecoveryGoogle Scholar
  18. 18.
    Korus P, Dziech A (2013) Efficient method for content reconstruction with self-embedding. IEEE Trans Image Process 22(3):1134–1147MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kumar A, Ghrera S, Tyagi V (2015) A new and efficient buyer-seller digital Watermarking protocol using identity based technique for copyright protection. In: 2015 Third International Conference on Image Information Processing (ICIIP). IEEE, pp 531–535Google Scholar
  20. 20.
    Kumar A, Ghrera S, Tyagi V (2017) An ID-based Secure and Flexible Buyer-seller Watermarking Protocol for Copyright Protection. Pertanika Journal of Science & Technology 25(1)Google Scholar
  21. 21.
    Lin PL, Hsieh C-K, Huang P-W (2005) A hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recogn 38(12):2519–2529CrossRefGoogle Scholar
  22. 22.
    Liu K-C (2012) Colour image watermarking for tamper proofing and pattern-based recovery. IET Image Process 6(5):445–454MathSciNetCrossRefGoogle Scholar
  23. 23.
    Mahdian B, Saic S (2010) Blind methods for detecting image fakery. IEEE Aerosp Electron Syst Mag 25(4):18–24CrossRefGoogle Scholar
  24. 24.
    Mehta R, Rajpal N, Vishwakarma VP (2018) Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization. Int J Mach Learn Cybern 9(1):145–161CrossRefGoogle Scholar
  25. 25.
    Nguyen T-S, Chang C-C, Yang X-Q (2016) A reversible image authentication scheme based on fragile watermarking in discrete wavelet transform domain. AEU-International Journal of Electronics and Communications 70(8):1055–1061CrossRefGoogle Scholar
  26. 26.
    Qian Z, Feng G, Zhang X, Wang S (2011) Image self-embedding with high-quality restoration capability. Digital Signal Processing 21(2):278–286CrossRefGoogle Scholar
  27. 27.
    Qin C, Ji P, Wang J, Chang C-C (2017) Fragile image watermarking scheme based on VQ index sharing and self-embedding. Multimed Tools Appl 76(2):2267–2287CrossRefGoogle Scholar
  28. 28.
    Qin C, Ji P, Zhang X, Dong J, Wang J (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138:280–293CrossRefGoogle Scholar
  29. 29.
    Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6:10269–10278CrossRefGoogle Scholar
  30. 30.
    Singh B, Sharma KD, Jatav LS (2014) New robust watermarking approach for image authentication. Paper presented at the Consumer Electronics (GCCE), 2014 IEEE 3rd Global Conference on,Google Scholar
  31. 31.
    Singh D, Shivani S, Agarwal S (2013) Self-embedding pixel wise fragile watermarking scheme for image authentication. In: Intelligent interactive technologies and multimedia. Springer, pp 111–122Google Scholar
  32. 32.
    Singh D, Singh SK (2016) Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J Vis Commun Image Represent 38:775–789CrossRefGoogle Scholar
  33. 33.
    Singh D, Singh SK (2017) DCT based efficient fragile watermarking scheme for image authentication and restoration. Multimed Tools Appl 76(1):953–977CrossRefGoogle Scholar
  34. 34.
    Suthaharan S (2004) Fragile image watermarking using a gradient image for improved localization and security. Pattern Recogn Lett 25(16):1893–1903CrossRefGoogle Scholar
  35. 35.
    Walia E, Suneja A (2014) A robust watermark authentication technique based on Weber’s descriptor. SIViP 8(5):859–872CrossRefGoogle Scholar
  36. 36.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  37. 37.
    Wong PW, Memon N (2001) Secret and public key image watermarking schemes for image authentication and ownership verification. IEEE Trans Image Process 10(10):1593–1601CrossRefGoogle Scholar
  38. 38.
    Yang H, Kot AC (2006) Binary image authentication with tampering localization by embedding cryptographic signature and block identifier. IEEE signal Processing Letters 13(12):741–744CrossRefGoogle Scholar
  39. 39.
    Yang C-W, Shen J-J (2010) Recover the tampered image based on VQ indexing. Signal Process 90(1):331–343CrossRefGoogle Scholar
  40. 40.
    Zhang X, Qian Z, Ren Y, Feng G (2011) Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction. IEEE Transactions on Information Forensics and Security 6(4):1223–1232CrossRefGoogle Scholar
  41. 41.
    Zhang X, Wang S (2007) Statistical fragile watermarking capable of locating individual tampered pixels. IEEE Signal Processing Letters 14(10):727–730CrossRefGoogle Scholar
  42. 42.
    Zhang X, Wang S (2008) Fragile watermarking with error-free restoration capability. IEEE Transactions on Multimedia 10(8):1490–1499CrossRefGoogle Scholar
  43. 43.
    Zhang X, Wang S, Qian Z, Feng G (2011) Reference sharing mechanism for watermark self-embedding. IEEE Trans Image Process 20(2):485–495MathSciNetCrossRefGoogle Scholar
  44. 44.
    Zhao Y, Chen Z, Zhu C, Tan Y-P, Yu L (2011) Binocular just-noticeable-difference model for stereoscopic images. IEEE Signal Processing Letters 18(1):19–22CrossRefGoogle Scholar
  45. 45.
    Zhou W, Yu L, Wang Z, Wu M, Luo T, Sun L (2016) Binocular visual characteristics based fragile watermarking scheme for tamper detection in stereoscopic images. AEU-International Journal of Electronics and Communications 70(1):77–84CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Radiation Engineering, National Center for Radiation Research and Technology (NCRRT)Egyptian Atomic Energy AuthorityCairoEgypt
  2. 2.School of Computing and InformationUniversity of PittsburghPittsburghUSA

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