An effective image self-recovery based fragile watermarking using self-adaptive weight-based compressed AMBTC


The quality of the watermarked image is degraded by introducing a large amount of the watermark, which also draws more attention of malicious attackers. Therefore, it is important to improve the quality of a watermarked image and improve the restoration capability of a tampered image. For this, a novel self-recovery based fragile watermarking scheme is proposed in this paper. To improve the watermarked image quality, a bit-reduction based AMBTC technology is employed to generate a watermark with fewer bits. The watermark is then embedded into the original image using turtle shell based data hiding technique. In the tampering detection phase, the high accuracy for tampering localization is achieved employing a two-level tampering detection strategy. Additionally, an effective self-adaptive weight-based recovery algorithm and an image inpainting algorithm are sequentially employed to provide improved recovered image quality. The experimental results show that the watermarked images appear to demonstrate higher quality (up to 49.76 dB), and the average PSNR of recovered images can be up to 34.65 dB, which is higher than that of the state-of-the-art methods.

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  1. 1.

    Alias Sathya SP, Ramakrishnan S (2020) Non-redundant frame identification and keyframe selection in DWT-PCA domain for authentication of video. IET Image Process 14(2):366–375

    Article  Google Scholar 

  2. 2.

    Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Transactions on Information Forensics and Security 7(4):1105–1117

    Article  Google Scholar 

  3. 3.

    Bertalmio M., Sapiro G., Caselles V., Ballester C., “Image inpainting,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. ACM Press/Addison-Wesley Publishing Company, 2000: 417–424.

  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):585–595

    Article  Google Scholar 

  5. 5.

    Chang C. C., Liu Y. J., Nguyen T. S., “A novel turtle shell based scheme for data hiding,” in Proceeding of 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), IEEE, 2014: 89–93.

  6. 6.

    Criminisi A., Perez P., Toyama K., “Object removal by exemplar-based inpainting,” in Proceedings of the 2003 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2003, 2: 1–8.

  7. 7.

    Delp EJ, Mitchell OR (1979) Image compression using block truncation coding. IEEE Trans Commun 27(9):1335–1342

    Article  Google Scholar 

  8. 8.

    Dube S, Sharma K (2019) Hybrid approach to enhance contrast of image for forensic investigation using segmented histogram. International Journal of Advanced Intelligence Paradigms 13(1–2):43–66

    Article  Google Scholar 

  9. 9.

    Gul E, Ozturk S (2019) A novel hash function based fragile watermarking method for image integrity. Multimed Tools Appl 78(13):17701–17718

    Article  Google Scholar 

  10. 10.

    Guzman AM, Goryawala M, Wang J, Barreto A, Andrian J, Rishe N, Adjouadi M (2013) Thermal imaging as a biometrics approach to facial signature authentication. IEEE Journal of Biomedical and Health Informatics 17(1):214–222

    Article  Google Scholar 

  11. 11.

    Hariyanto E, Rahim R (2016) Arnold’s cat map algorithm in digital image encryption. International Journal of Science and Research (IJSR) 5(10):1363–1365

    Article  Google Scholar 

  12. 12.

    Hemida O, He H (2020) A self-recovery watermarking scheme based on block truncation coding and quantum chaos map. Multimed Tools Appl:1–31

  13. 13.

    Hemida O, Huo Y, He H, Chen F (2019) A restorable fragile watermarking scheme with superior localization for both natural and text images. Multimed Tools Appl 78(9):12373–12403

    Article  Google Scholar 

  14. 14.

    Hesabi S., Jamzad M., Mahdavi-Amiri N., “Structure and texture image inpainting,” in Proceeding of 2010 International Conference on Signal and Image Processing, 2010: 119–124.

  15. 15.

    Jiao S, Zhou C, Shi Y, Zou W, Li X (2019) Review on optical image hiding and watermarking techniques. Opt Laser Technol 109:370–380

    Article  Google Scholar 

  16. 16.

    Kim C, Shin D, Yang CN (2018) Self-embedding fragile watermarking scheme to restoration of a tampered image using AMBTC. Pers Ubiquit Comput 22(1):11–22

    Article  Google Scholar 

  17. 17.

    Lee TY, Lin SD (2008) Dual watermark for image tamper detection and recovery. Pattern Recogn 41(11):3497–3506

    MathSciNet  Article  Google Scholar 

  18. 18.

    Lema MD, Mitchell OR (1984) Absolute moment block truncation coding and its application to color images. IEEE Trans Commun 32(10):1148–1157

    Article  Google Scholar 

  19. 19.

    Lin PL, Hsieh CK, Huang PW (2005) A hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recogn 38(12):2519–2529

    Article  Google Scholar 

  20. 20.

    Lu CS, Liao HYM (2003) Structural digital signature for image authentication: an incidental distortion resistant scheme. IEEE Transactions on Multimedia 5(2):161–173

    Article  Google Scholar 

  21. 21.

    Lu H, Shen R, Chung FL (2003) Fragile watermarking scheme for image authentication. Electron Lett 39(12):898–900

    Article  Google Scholar 

  22. 22.

    Molina-Garcia J, Garcia-Salgado BP, Ponomaryov V, Reyes-Reyes R, Sadovnychiy S, Cruz-Ramos C (2020) An effective fragile watermarking scheme for color image tampering detection and self-recovery. Signal Process Image Commun 81:115725

    Article  Google Scholar 

  23. 23.

    Qian Z, Feng G, Zhang X, Wang S (2001) Image self-embedding with high-quality restoration capability. Digital Signal Processing 21(2):278–286

    Article  Google Scholar 

  24. 24.

    Schneider M., Chang S. F., “A robust content based digital signature for image authentication,” in Proceedings of 3rd IEEE International Conference on Image Processing, 1996, 3: 227–230.

  25. 25.

    Sharma K, Bala S, Bansal H, Shrivastava G (2017) Introduction to the special issue on secure solutions for network in scalable computing. Scalable Computing: Practice and Experience 18(3):iii–iv

    Google Scholar 

  26. 26.

    Shehab A, Elhoseny M, Muhammad K, Sangaias AK, Yang B, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6:10269–10278

    Article  Google Scholar 

  27. 27.

    Shrivastava G., Pandey A., Sharma K., “Steganography and its technique: technical overview,” in Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Springer, 2013: 615–620.

  28. 28.

    Shrivastava G., Gia N. N., Bouhlel M. S., Sharma K., “Special issue on advance research in model driven security, privacy, and forensic of smart devices preface,” 2018.

  29. 29.

    Shrivastava G, Kumar P, Gupta BB, Bala S, Dey N (2018) Handbook of research on network forensics and analysis techniques. IGI Global

  30. 30.

    Su GD, Xu SW, Cai BL (2018) Indoor positioning method based on dynamic centroid iteration and error correction. Computer Systems and Applications 27(11):265–270

    Google Scholar 

  31. 31.

    Su GD, Liu Y, Chang CC (2019) A square lattice oriented reversible information hiding scheme with reversibility and adaptivity for dual images. J Vis Commun Image Represent 64:102618

    Article  Google Scholar 

  32. 32.

    Walton S., “Image authentication for a slippery new age,” Dr. Dobb's Journal, 1995, 20(4): 18–26.

  33. 33.

    Wong PW (1998) A watermark for image integrity and ownership verification. Is and Ts Pics Conference Society for Imaging Science & Technology:374–379

  34. 34.

    Wong PW, Memon N (2001) Secret and public key image watermarking schemes for image authentication and ownership verification. IEEE Trans Image Process 10(10):1593–1601

    Article  Google Scholar 

  35. 35.

    Wu L., Zhang J., Deng W., He D., “Arnold transformation algorithm and anti-arnold transformation algorithm,” in Proceeding of 2009 1st International Conference on Information Science and Engineering (ICISE), IEEE, 2009: 1164–1167.

  36. 36.

    Xu L, Zhang JQ, Yan Y (2004) A wavelet-based multisensor data fusion algorithm. IEEE Trans Instrum Meas 53(6):1539–1545

    Article  Google Scholar 

  37. 37.

    Yan C. P., Pun C. M., Yuan X. C, “Multi-scale image hashing using adaptive local feature extraction for robust tampering detection,” Signal Process, 2016, 121: 1–16.

  38. 38.

    Yeung M. M., Mintzer F., “An invisible watermarking technique for image verification,” in Proceedings of International Conference on Image Processing, 1997, 2: 680–683.

  39. 39.

    Zhang X, Wang S (2008) Fragile watermarking with error-free restoration capability. IEEE Transactions on Multimedia 10(8):1490–1499

    Article  Google Scholar 

  40. 40.

    Zhang X, Wang S, Feng G (2009) Fragile watermarking scheme with extensive content restoration capability. International Workshop on Digital Watermarking:268–278

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This work was supported by the Open Fund of Engineering Research Center for ICH Digitalization and Multi-Source Information Fusion of Fujian Province (FJ-ICH201901), the Education-Scientific Research Project for Middle-Aged and Young of Fujian Province (JAT160574, JT180621 and JAT190488), and the Natural Science Foundation of Fujian Province.

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Chang, C., Lin, C. & Su, G. An effective image self-recovery based fragile watermarking using self-adaptive weight-based compressed AMBTC. Multimed Tools Appl (2020).

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  • Fragile watermarking
  • Self-recovery
  • Bit-reduction based AMBTC technology
  • Self-adaptive weight