Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

Deep Learning Framework for Watermark-Adaptive and Resolution-Adaptive Image Watermarking

워터마크 및 해상도 적응적인 영상 워터마킹을 위한 딥 러닝 프레임워크

  • Lee, Jae-Eun (Department of Electronic Materials Engineering, Kwangwoon University) ;
  • Seo, Young-Ho (Department of Electronic Materials Engineering, Kwangwoon University) ;
  • Kim, Dong-Wook (Department of Electronic Materials Engineering, Kwangwoon University)
  • Received : 2019.12.26
  • Accepted : 2020.01.30
  • Published : 2020.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, application fields for processing and using digital image contents in various forms and types are rapidly increasing. Since image content is high value-added content, the intellectual property rights of this content must be protected in order to activate the production and use of the digital image content. In this paper, we propose a deep learning based watermark embedding and extraction network. The proposed method is to maximize the robustness of the watermark against malicious/non-malicious attacks while preserving the invisibility of the host image. This network consists of a preprocessing network that changes the watermark to have the same resolution as the host image, a watermark embedding network that embeds watermark data while maintaining the resolution of the host image by three-dimensionally concatenating the changed host image and the watermark information, and a watermark extraction network that reduces the resolution and extracts watermarks. This network verifies the invisibility and robustness of the proposed method by experimenting with various pixel value change attacks and geometric attacks against various watermark data and host images with various resolutions, and shows that this method is universal and practical.

최근 다양한 형태와 종류로 영상 콘텐츠를 가공하고 사용하는 응용분야가 급격히 증가하고 있다. 영상 콘텐츠는 고부가가치의 콘텐츠이므로 영상 콘텐츠의 제작 및 사용이 활성화되기 위해서는 이 콘텐츠의 지적재산권이 보호되어야 하며, 현재까지 그 방법으로 가장 널리 연구되고 있는 것이 디지털 워터마킹이다. 이에 본 논문에서는 딥 러닝 기반의 워터마크 삽입 및 추출 네트워크를 제안한다. 제안하는 방법은 호스트 영상의 비가시성(invisibility)을 보존하면서 악의적/비악의적 공격에 워터마크의 강인성(robustness)를 극대화하는 방법이다. 이 네트워크는 워터마크를 호스트 영상과 똑같은 해상도를 갖도록 변화시키는 전처리 네트워크, 변화된 호스트 영상과 워터마크 정보를 3차원적으로 정합하여 호스트 영상의 해상도를 유지하면서 워터마크 데이터를 삽입하는 네트워크, 그리고 해상도를 줄이며 워터마크를 추출하는 네트워크로 구성된다. 이 네트워크는 다양한 워터마크 영상과 다양한 해상도를 가진 호스트 영상에 대해 다양한 화소값 변경공격과 기하학적 공격을 실험하여 제안하는 방법의 비가시성과 강인성을 검증하고, 이 방법이 범용적이고 실용적임을 보인다.

Keywords

References

  1. I. J. Cox, et al., "Digital watermarking and steganigraphy," Morgan Kaufmann Publisher, 2008.
  2. X. Kang , J. Huang , Y.Q. Shi , Y. Lin , "A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression," IEEE Trans. Circ. Syst. Video Technol. Vol.13, No.8, pp. 776-786, 2003. https://doi.org/10.1109/TCSVT.2003.815957
  3. J. George, S. Varma and M. Chatterjee, "Color image watermarking using DWT-SVD and Arnold transform," India Conference (INDICON), pp. 1-6, Dec 2014.
  4. Y. S. Lee, Y. H. Seo, and D. W. Kim, "Blind image watermarking based on adaptive data spreading in n-level DWT subbands," Security and Communication Networks, Vol.2019, Feb., 2019, http://dx.doi. org/10.1155/2019/8357251
  5. C. Li, Z. Zhang, Y. Wang, B. Ma, D. Huang, "Dither modulation of significant amplitude difference for wavelet based robust watermarking," Neurocomputing, Vol.166, pp. 404-415, 2015. https://doi.org/10.1016/j.neucom.2015.03.039
  6. J. Ouyang, G. Coatrieux, B. Chen and H. Shu, "Color image watermarking based on quaternion Fourier transform and improved uniform log-polar mapping," Computers & Electrical Engineering, http://dx.doi. org/10.1016/ j.compeleceng.2015.03.004. 419-432 (2015).
  7. R. Mehta, V. P. Vishwakarma, and N. Rajpal, "Lagrangian support vector regression based image watermarking in wavelet domain," in International Conference on SPIN, Noida, Delhi-NCR, India, pp. 854-859, Feb., 2015.
  8. H. Hu, Y. Chang, and S. Chen, "A progressive QIM to cope with SVD-based blind image watermarking in DWT domain," IEEE China Summit & International Conference on Signal and Information Processing, Xi'an, China, pp. 421-425, July 2014.
  9. H. Kandi, D. Mishra, S.R.S. Gorthi, "Exploring the learning capabilities of convolutional neural networks for robust image watermarking," Comput. Secur. Vol. 65, pp. 247-268, 2017. https://doi.org/10.1016/j.cose.2016.11.016
  10. J. Zhu, R. Kaplan, J. Johnson, and L. Fei-Fei, "HiDDeN: hiding data with deep networks," arXiv:1807.09937, July, 2018
  11. M. Ahmadi, A. Norouzi, S. M. Reza Soroushmehr, N. Karimi, K. Najarian, S. Samavi, and A. Emami, "ReDMark: framework for residual diffusion watermarking on deep networks," arXiv:1810.07248, Dec. 2018.
  12. S. M. Mun, S. H. Nam, H. Jang, D. Kim, and H. K. Lee, "Finding robust domain from attacks: a learning framework for blind watermarking," Neurocomputing, Vol.337, No.14, pp.191-202, April, 2019, https://doi.org/10.1016/j.neucom.2019.01.067
  13. X. Zhong, and F. Y. Shih, "A robust image watermarking system based on deep neural networks," arXiv:1908.11331, Aug., 2019.
  14. Y. Liu, M. Guo, J. Zhang, Y. Zhu, and X. Xie, "A novel two-stage separable deep learning framework for practical blind watermarking," in Proceedings of the 27th ACM International Conference on Multimedia, France, Oct., 2019.
  15. Bingyang Wen, and Sergul Aydore, "ROMark, a robust watermarking system using adversarial training," arXiv:1910.01221, Oct., 2019.
  16. Bas, P., Filler, T., Pevny, T., "Break our steganographic system: the ins and outs of organizing BOSS," International Workshop on Information Hiding, Springer, pp. 59-70, 2011.
  17. Dataset of standard 512x512 grayscale test images. URL http://decsai.ugr.es/cvg/CG/base.htm