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Real-time attacks on robust watermarking tools in the wild by CNN

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Abstract

Robust watermarking is a widely used technology to protect image copyright. Robustness, the ability to resist various distortions, is the most important property of robust watermarking algorithm. So to improve the robustness of the watermarking schemes, watermark attacking algorithms also attract much attention. Far from now, the existing watermarking attack methods cannot well balance the removal ability and visual quality. To address this issue, this paper proposed a removal attack by a convolutional neural network (CNN). Considering the speed requirements of real-time attack applications, for short computing time, we use a simple but powerful CNN. According to the amount of knowledge of watermarking, a corresponding dataset of watermark images is constructed. After that, the CNN model is trained to remove watermark with these datasets. The experiments show that the trained model can not only effectively remove the watermark, but also recover the original image without much image quality degradation.

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References

  1. Zhou, Q., Qiu, Y., Li, L., Lu, J., Yuan, W., Feng, X., Mao, X.: Steganography using reversible texture synthesis based on seeded region growing and LSB. Comput. Mater. Continua 55(1), 151 (2018)

    Article  Google Scholar 

  2. Zhou, Z., Mu, Y., Wu, Q.J.: Coverless image steganography using partial-duplicate image retrieval. Soft Comput. 23(13), 4927 (2019)

    Article  Google Scholar 

  3. Voloshynovskiy, S., Pereira, S., Iquise, V., Pun, T.: Attack modelling: towards a second generation watermarking benchmark. Signal Process. 81(6), 1177 (2001)

    Article  Google Scholar 

  4. Su, J.K., Eggers, J.J., Girod, B.: Optimum attack on digital watermarks and its defense. In: IEEE Conference Record of the Thirty-fourth Asilomar Conference on Signals, Systems and Computers (Cat. No. 00CH37154), vol. 2, pp. 1836–1840 (2000)

  5. Tao, H., Chongmin, L., Zain, J.M., Abdalla, A.N.: Robust image watermarking theories and techniques: a review. J. Appl. Res. Technol. 12(1), 122 (2014)

    Article  Google Scholar 

  6. Parah, S.A., Sheikh, J.A., Loan, N.A., Bhat, G.M.: Robust and blind watermarking technique in DCT domain using inter-block coefficient differencing. Digit. Signal Process. 53, 11 (2016)

    Article  Google Scholar 

  7. Fang, H., Zhang, W., Zhou, H., Cui, H., Yu, N.: Screen-shooting resilient watermarking. IEEE Trans. Inf. Forensics Secur. 14(6), 1403 (2019)

    Article  Google Scholar 

  8. Kang, X., Huang, J., Zeng, W.: Efficient general print-scanning resilient data hiding based on uniform log-polar mapping. IEEE Trans. Inf. Forensics Secur. 5(1), 1 (2010)

    Article  Google Scholar 

  9. Li, C., Zhang, Z., Wang, Y., Ma, B., Huang, D.: Dither modulation of significant amplitude difference for wavelet based robust watermarking. Neurocomputing 166, 404 (2015)

    Article  Google Scholar 

  10. Wang, Y., Ni, R., Zhao, Y., Xian, M.: Watermark embedding for direct binary searched halftone images by adopting visual cryptography. Comput. Mater. Continua 55(2), 255 (2018)

    Article  Google Scholar 

  11. Voloshynovskiy, S., Pereira, S., Pun, T., Eggers, J.J., Su, J.K.: Attacks on digital watermarks: classification, estimation based attacks, and benchmarks. IEEE Commun. Mag. 39(8), 118 (2001)

    Article  Google Scholar 

  12. D’angelo, A., Barni, M., Merhav, N.: Stochastic image warping for improved watermark desynchronization. Eur. J. Inf. Syst. 2008(1), 1 (2008)

    Google Scholar 

  13. Yan, B., Liu, X.F., Yang, H.M.: Structure compliant local warping of images with applications to watermarking attack. Multimed. Tools Appl. 75(21), 13451 (2016)

    Article  Google Scholar 

  14. Barni, M.: Effectiveness of exhaustive search and template matching against watermark desynchronization. IEEE Signal Process. Lett. 12(2), 158 (2005)

    Article  MathSciNet  Google Scholar 

  15. Pereira, S., Pun, T.: Fast robust template matching for affine resistant image watermarks. In: Int. workshop on Inf. Hiding. Springer, Berlin, Heidelberg, pp. 199–210 (1999).

  16. Deguillaume, F., Csurka, G., Pun, T.: Countermeasures for unintentional and intentional video watermarking attacks. In: Security and Watermarking of Multimedia Contents II. International Society for Optics and Photonics, vol. 3971, pp. 346–357 (2000).

  17. Petitcolas, F. A., Anderson, R. J., & Kuhn, M. G.: Attacks on copyright marking systems. In: Int. workshop on Inf. Hiding. Springer, Berlin, Heidelberg, pp. 218–238 (1998)

  18. Langelaar, G.C., Lagendijk, R.L., Biemond, J.: Removing spatial spread spectrum watermarks by non-linear filtering. In: IEEE 9th European Signal Processing Conference. (EUSIPCO 1998), pp. 1–4 (1998)

  19. Moulin, P., O’Sullivan, J.A.: Information-theoretic analysis of information hiding. IEEE Trans. Inf. Theory 49(3), 563 (2003)

    Article  MathSciNet  Google Scholar 

  20. Hong, S., Wu, M., Li, H., Wu, Z.: Event2vec: Learning representations of events on temporal sequences. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, pp. 33–47. Springer, (2017)

  21. Hong, S., Zhou, Y., Wu, M., Shang, J., Wang, Q., Li, H., Xie, J.: Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings. Physiol. Meas. 40(5), 054009 (2019)

    Article  Google Scholar 

  22. Jain, V., Seung, H.S.: Natural image denoising with convolutional networks. In: NIPS, pp. 769–776 (2008)

  23. Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399 (2012)

  24. Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256 (2017)

    Article  Google Scholar 

  25. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142 (2017)

    Article  MathSciNet  Google Scholar 

  26. Li, C., Jiang, Y., Cheslyar, M.: Computers. Mater. Continua 56(2), 313 (2018)

    Google Scholar 

  27. Bae, W., Yoo, J., Chul Ye, J.: Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1141–1149 (2017)

  28. Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: risk bounds and structural results. J. Mach. Learn. Res. 3(Nov), 463 (2002)

    MathSciNet  MATH  Google Scholar 

  29. Telgarsky, M.: Benefits of depth in neural networks. arXiv preprint arXiv:1602.04485 (2016)

  30. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

  31. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics (AISTATS). pp. 315–323 (2011)

  32. Bas, P., Filler, T., Pevný, T.: Break our steganographic system: The Ins and outs of organizing BOSS. In: Int. Workshop Inf. Hiding. Springer, Berlin, Heidelberg, pp. 59–70 (2011)

  33. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  34. Chollet, F. et al.: Keras (2015). https://github.com/fchollet/keras

  35. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X., Brain, G.: TensorFlow: a system for large-scale machine learning. In: OSDI, pp. 265–284 (2016)

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants U1636201 and 61572452, and by Anhui Initiative in Quantum Information Technologies under Grant AHY150400.

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Authors and Affiliations

  1. CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei, Anhui, China

    Linfeng Geng, Weiming Zhang, Haozhe Chen, Han Fang & Nenghai Yu

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  1. Linfeng Geng
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  2. Weiming Zhang
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  3. Haozhe Chen
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  4. Han Fang
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  5. Nenghai Yu
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Correspondence to Weiming Zhang.

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Geng, L., Zhang, W., Chen, H. et al. Real-time attacks on robust watermarking tools in the wild by CNN. J Real-Time Image Proc 17, 631–641 (2020). https://doi.org/10.1007/s11554-020-00941-8

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  • DOI: https://doi.org/10.1007/s11554-020-00941-8

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