An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Robust optimum detection of transform domain multiplicative watermarks
IEEE Transactions on Signal Processing
Optimum decoding and detection of multiplicative watermarks
IEEE Transactions on Signal Processing
An additive approach to transform-domain information hiding andoptimum detection structure
IEEE Transactions on Multimedia
Image-adaptive watermarking using visual models
IEEE Journal on Selected Areas in Communications
Visibility of wavelet quantization noise
IEEE Transactions on Image Processing
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A new decoder for the optimum recovery of nonadditive watermarks
IEEE Transactions on Image Processing
Asymptotically optimal detection for additive watermarking in the DCT and DWT domains
IEEE Transactions on Image Processing
Locally optimum nonlinearities for DCT watermark detection
IEEE Transactions on Image Processing
Optimal watermark detection under quantization in the transform domain
IEEE Transactions on Circuits and Systems for Video Technology
Statistical robustness in multiplicative watermark detection
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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Deviations of the actual coefficient distributions from the idealized theoretical models due to inherent modeling errors and possible attacks are big challenges for watermark detection. These uncertain deviations may degrade or even upset the performance of existing optimum detectors that are optimized at idealized models. In this paper, we present a new detection structure for transform domain additive watermarks based on Huber’s robust hypothesis testing theory. The statistical behaviors of the image subband coefficients are modeled by a contaminated generalized Gaussian distribution (GGD), which tries to capture small deviations of the actual situation from the idealized GGD. The robust detector is a min-max solution of the contamination model and turns out to be a censored version of the optimum probability ratio test. Experimental results on real images confirm the superiority of the proposed detector with respect to the classical optimum detector.