An additive approach to transform-domain information hiding and optimum detection structure

This paper presents an additive approach to transform-domain information hiding and the performance analysis for images and video. The watermark embedding method is designed to satisfy the perceptual constraints and improve the detectability as well as the information embedding rate. The statistical behaviors of subband coefficients are modeled by the generalized Gaussian distribution. The structure of the optimum detection is built and the performance of the exact asymptotic detection is evaluated using large deviation theory. Our approach can not only achieve good transparency but also precisely control the detection errors. It can be applied to watermarking, authentication, fingerprinting, and steganography.

[1]  G.W. Wornell,et al.  An information-theoretic approach to the design of robust digital watermarking systems , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[2]  Fernando Pérez-González,et al.  Statistical analysis of watermarking schemes for copyright protection of images , 1999, Proc. IEEE.

[3]  Ingemar J. Cox,et al.  Secure spread spectrum watermarking for multimedia , 1997, IEEE Trans. Image Process..

[4]  Fernando Pérez-González,et al.  DCT-domain watermarking techniques for still images: detector performance analysis and a new structure , 2000, IEEE Trans. Image Process..

[5]  John B. Thomas,et al.  Detectors for discrete-time signals in non-Gaussian noise , 1972, IEEE Trans. Inf. Theory.

[6]  Ricardo L. de Queiroz,et al.  Nonexpansive pyramid for image coding using a nonlinear filterbank , 1998, IEEE Trans. Image Process..

[7]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[8]  Thierry Pun,et al.  Robust template matching for affine resistant image watermarks , 2000, IEEE Trans. Image Process..

[9]  Frank Hartung,et al.  Multimedia watermarking techniques , 1999, Proc. IEEE.

[10]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Wenjun Zeng,et al.  A statistical watermark detection technique without using original images for resolving rightful ownerships of digital images , 1999, IEEE Trans. Image Process..

[12]  Joseph A. O'Sullivan,et al.  Information-theoretic analysis of watermarking , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[13]  R. R. Clarke.  Transform coding of images , 1985 .

[14]  Ahmed H. Tewfik,et al.  Multiresolution scene-based video watermarking using perceptual models , 1998, IEEE J. Sel. Areas Commun..

[15]  Edward J. Delp,et al.  Perceptual watermarks for digital images and video , 1999, Electronic Imaging.

[16]  Eero P. Simoncelli,et al.  Image compression via joint statistical characterization in the wavelet domain , 1999, IEEE Trans. Image Process..

[17]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[18]  Gordon W. Braudaway,et al.  Automatic recovery of invisible image watermarks from geometrically distorted images , 2000, J. Electronic Imaging.

[19]  John D. Villasenor,et al.  Visibility of wavelet quantization noise , 1997, IEEE Transactions on Image Processing.

[20]  Ioannis Pitas,et al.  Digital image watermarking using mixing systems , 1998, Comput. Graph..

[21]  Gerald S. Rogers,et al.  Mathematical Statistics: A Decision Theoretic Approach , 1967 .

[22]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[24]  G. W. Snedecor.  Statistical Methods , 1964 .

[25]  Benoît Macq,et al.  Special issue on identification and protection of multimedia information , 1999 .

[26]  Deepa Kundur,et al.  Digital watermarking using multiresolution wavelet decomposition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[27]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[28]  Andrew B. Watson,et al.  DCT quantization matrices visually optimized for individual images , 1993, Electronic Imaging.

[29]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[30]  Thomas S. Huang,et al.  Blind digital watermarking for images and videos and performance analysis , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[31]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[32]  Andrew B. Watson,et al.  Visually optimal DCT quantization matrices for individual images , 1993, [Proceedings] DCC `93: Data Compression Conference.

[33]  Markus G. Kuhn,et al.  Attacks on Copyright Marking Systems , 1998, Information Hiding.

[34]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[35]  Bernd Girod,et al.  Digital watermarking of text, image, and video documents , 1998, Comput. Graph..

[36]  Wenjun Zeng,et al.  Image-adaptive watermarking using visual models , 1998, IEEE J. Sel. Areas Commun..