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Fragile watermarking for image authentication using BRINT and ELM

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Abstract

Privacy and security concerns regarding digital information have been increasing with advancements in multimedia and network technologies. Digital images are an integral component of online-distributed content, which plays a crucial role in forming public perceptions and opinions. They also play a major role in sensitive areas, such as law and defense. Any attempt to tamper with them is, therefore, a serious issue. One common approach to this problem is fragile image watermarking, which is used to ensure the authenticity of digital content. In this paper, a new fragile watermarking method for the authentication of digital images is proposed based on a binary rotation invariant and noise tolerant (BRINT) local texture descriptor and an extreme learning machine (ELM). BRINT is used to generate and retrieve the watermark in both the embedding and extraction procedures. In parallel, ELM is used in both procedures to learn and recover any tampered areas. The experimental results showed that the proposed scheme does not degrade image quality, allows for tamper detection, and has a recovery ability comparable with state-of-the-art fragile and semi-fragile watermarking schemes. Moreover, the proposed scheme has been validated as a fragile watermarking method with the potential to detect and locate modifications in digital images, such as copy-paste forgery, JPEG compression, and noise addition. This method is useful in sensitive fields, like defense, law, and journalism, in which decision-making based on digital visual information is necessary, to ensure that an image is authentic and has not been tampered with.

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References

  1. Al-Nabhani Y et al (2015) Robust watermarking algorithm for digital images using discrete wavelet and probabilistic neural network. J King Saud Univ Comput Inf Sci 27(4):393–401

    Google Scholar 

  2. Aslantas V, Dogru M (2015) "A new SVD based fragile image watermarking by using genetic algorithm," In Sixth International Conference on Graphic and Image Processing (ICGIP 2014), vol. 9443, p. 94431H: International Society for Optics and Photonics

  3. Chalamala SR, Kakkirala KR (2015) "Local Binary Patterns for Digital Image Watermarking," In: Artificial Intelligence, Modelling and Simulation (AIMS), 2015 3rd International Conference on, pp. 159–162: IEEE

  4. Chang J-D, Chen B-H, Tsai C-S (2013) "LBP-based fragile watermarking scheme for image tamper detection and recovery," In: Next-Generation Electronics (ISNE), 2013 IEEE International Symposium on, pp. 173–176: IEEE

  5. Chen C-M, Xiang B, Liu Y, Wang K-H (2019) A secure authentication protocol for internet of vehicles. IEEE Access 7:12047–12057

    Article  Google Scholar 

  6. Das D, Nayak DR, Dash R, Majhi B (2019) An empirical evaluation of extreme learning machine: application to handwritten character recognition. Multimed Tools Appl:1–29

  7. Dong J, Wang W, Tan T (2013) "Casia image tampering detection evaluation database," In: Signal and Information Processing (ChinaSIP), 2013 IEEE China Summit & International Conference on, pp. 422–426: IEEE

  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):619–626

    Article  Google Scholar 

  9. Haghighi BB, Taherinia AH, Harati A (2018) TRLH: fragile and blind dual watermarking for image tamper detection and self-recovery based on lifting wavelet transform and halftoning technique. J Vis Commun Image Represent 50:49–64

    Article  Google Scholar 

  10. Hsieh M-S (2010) A robust image authentication method based on wavelet transform and Teager energy operator. Inte J Multimed Appl 2(3):1–17

    Google Scholar 

  11. Hsu C-S, Tu S-F (2016) Image tamper detection and recovery using adaptive embedding rules. Measurement 88:287–296

    Article  Google Scholar 

  12. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  13. Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529

    Article  Google Scholar 

  14. Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529

    Article  Google Scholar 

  15. Lin ET, Delp EJ (1999) "A review of fragile image watermarks," In: Proceedings of the Multimedia and Security Workshop (ACM Multimedia'99) Multimedia Contents, vol. 1, pp. 25–29: Citeseer

  16. Lin P-Y, Lee J-S, Chang C-C (2011) Protecting the content integrity of digital imagery with fidelity preservation. ACM Trans Multimed Comput Commun Appl (TOMM) 7(3):15

    Google Scholar 

  17. Liu L, Long Y, Fieguth PW, Lao S, Zhao G (2014) BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process 23(7):3071–3084

    Article  MathSciNet  Google Scholar 

  18. Liu X-L, Lin C-C, Chang C-C, Yuan S-M (2016) A survey of fragile watermarking-based image authentication techniques. J Inf Hiding Multimedia Signal Process 7(6):1282–1292

    Google Scholar 

  19. Nayak DR, Dash R, Majhi B (2018) Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 282:232–247

    Article  Google Scholar 

  20. Nayak DR, Dash R, Majhi B (2018) Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-II transform. Multimed Tools Appl 77(17):22705–22733

    Article  Google Scholar 

  21. Nayak DR, Das D, Dash R, Majhi S, Majhi B (2019) Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images. Multimed Tools Appl:1–16

  22. Pinjari SA, Patil NN (2016) "A pixel based fragile watermarking technique using LBP (Local Binary Pattern)," In: Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016 International Conference on, pp. 194–196: IEEE

  23. 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 138:280–293

    Article  Google Scholar 

  24. Qin C, Ji P, Chang C-C, Dong J, Sun X (2018) Non-uniform watermark sharing based on optimal iterative BTC for image tampering recovery. IEEE MultiMedia 25(3):36–48

    Article  Google Scholar 

  25. Rosales-Roldan L, Cedillo-Hernandez M, Nakano-Miyatake M, Perez-Meana H, Kurkoski B (2013) Watermarking-based image authentication with recovery capability using halftoning technique. Signal Process Image Commun 28(1):69–83

    Article  Google Scholar 

  26. Singh P, Agarwal S (2016) An efficient fragile watermarking scheme with multilevel tamper detection and recovery based on dynamic domain selection. Multimed Tools Appl 75(14):8165–8194

    Article  Google Scholar 

  27. Singh P, Agarwal S (2017) A self recoverable dual watermarking scheme for copyright protection and integrity verification. Multimed Tools Appl 76(5):6389–6428

    Article  Google Scholar 

  28. Singh D, Singh SK (2016) Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J Vis Commun Image Represent 38:775–789

    Article  Google Scholar 

  29. Singh D, Singh SK (2017) DCT based efficient fragile watermarking scheme for image authentication and restoration. Multimed Tools Appl 76(1):953–977

    Article  Google Scholar 

  30. Som S, Palit S, Dey K, Sarkar D, Sarkar J, Sarkar K (2015) A DWT-based digital watermarking scheme for image tamper detection, localization, and restoration. Appl Comput Secur Syst: Springer 305:17–37

    Google Scholar 

  31. Tong X, Liu Y, Zhang M, Chen Y (2013) A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Signal Process Image Commun 28(3):301–308

    Article  Google Scholar 

  32. Walia E, Suneja A (2013) Fragile and blind watermarking technique based on Weber's law for medical image authentication. IET Comput Vis 7(1):9–19

    Article  Google Scholar 

  33. Wenyin Z, Shih FY (2011) Semi-fragile spatial watermarking based on local binary pattern operators. Opt Commun 284(16–17):3904–3912

    Article  Google Scholar 

  34. Wu T-Y, Chen C-M, Wang K-H, Meng C, Wang EK (2019) A provably secure certificateless public key encryption with keyword search. J Chin Inst Eng 42(1):20–28

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia for funding this work through the Research Group No. RGP-1439-067.

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Authors and Affiliations

  1. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Laila AlShehri, Muhammad Hussain & Hatim Aboalsamh

  2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Abdul Wadood

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  1. Laila AlShehri
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  2. Muhammad Hussain
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  3. Hatim Aboalsamh
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  4. Abdul Wadood
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Correspondence to Muhammad Hussain.

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AlShehri, L., Hussain, M., Aboalsamh, H. et al. Fragile watermarking for image authentication using BRINT and ELM. Multimed Tools Appl 79, 29199–29223 (2020). https://doi.org/10.1007/s11042-020-09441-0

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  • DOI: https://doi.org/10.1007/s11042-020-09441-0

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