A new pixel-wise mask for watermarking

ABSTRACT

Robust watermarking using pixel-wise masking in the wavelet domain proves to be quite robust against common signal processing. However, because embedding is made only in the highest resolution level, the watermark information can be easily erased by a potential attacker. In this paper, we propose a modified perceptual mask that models the human visual system behavior in a better way. The texture content is appreciated with the local standard deviation of the original image, which is further compressed in the wavelet domain. Since the approximation image of the coarsest level contains too little information, we appreciate the luminance content using a higher resolution level approximation subimage. Embedding is made in all the detail subbands except the coarsest level, for attack resilience; we propose three types of detectors that take advantage of the wavelet hierarchical decomposition. Tests were made for different attacks (JPEG compression, median filtering, resizing, cropping, gamma correction and blurring), that prove the effectiveness of the new pixel-wise mask in comparison with the previous system.

REFERENCES

• 1: Cox, I. Robust watermarking, ECRYPT Summer School on Multimedia Security, Salzburg, Austria, Sept. 22, 2005.
• 2: Cox, I., Killian, J., Leighton, T., Shamoon, T. Secure Spread Spectrum Watermarking for Multimedia, IEEE Trans. Image Processing, 6, 12, 1997, 1673--1687.
• 3: Podilchuk, C., Zeng, W. Image-Adaptive Watermarking Using Visual Models, IEEE Journal on Selected Areas in Communications, 16, 4, May 1998, 525--539.
• 4: Nafornita, C., Isar, A., Borda, M. Image Watermarking Based on the Discrete Wavelet Transform Statistical Characteristics, Proc. IEEE EUROCON 2005, Serbia & Montenegro, 943--946.
• 5: Barni, M., Bartolini, F., Piva, A. Improved wavelet-based watermarking through pixel-wise masking, IEEE Trans. Image Processing, 10, 5, May 2001, 783--791.
• 6: Kundur, D. Water-filling for Watermarking?, Proc. IEEE Int. Conf. Multimedia and Expo, NY, 1287--1290, Aug. 2000.
• 7: Fridrich, J., Goljan, M. Comparing robustness of watermarking techniques, SPIE, Security and Watermarking of Multimedia Contents, Vol. 3657, April 1999, 214--225.
• 8: Sendur, L., Selesnick, I. W. Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency, IEEE Trans. Signal Processing, 50, 11, Nov. 2002, 2744--2756.
• 9: Weber, A. G. The USC-SIPI image database: Version 5, available at http://sipi.usc.edu/database, Signal and Image Processing Institute at the University of Southern California, Sept. 1997.
• 10: Steven M. Kay, Fundamentals of statistical signal processing: estimation theory, Prentice-Hall, Inc., Upper Saddle River, NJ, 1993

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.9 Applications

General Terms:
Algorithms, Design, Performance, Security, Verification

Keywords:
pixel-wise mask, robust watermark, wavelets