A lightweight rao-cauchy detector for additive watermarking in the dwt-domain
Proceedings of the 10th ACM workshop on Multimedia and security
A patch-based structural masking model with an application to compression
Journal on Image and Video Processing - Special issue on patches in vision
Robust MC-CDMA-based fingerprinting against time-varying collusion attacks
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Image Processing
Image quality monitoring using spread spectrum watermarking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Asymptotically optimum universal watermark embedding and detection in the high-SNR regime
IEEE Transactions on Information Theory
Novel robust image watermarking based on subsampling and DWT
Multimedia Tools and Applications
Digital Image Authentication: A Review
International Journal of Digital Library Systems
Perceptual watermarking using a new Just-Noticeable-Difference model
Image Communication
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Digital watermarking is an efficient and promising approach to protect intellectual property rights of digital media. Spread spectrum (SS) is one of the most widely used image watermarking schemes because of its robustness against attacks and its support for the exploitation of the properties of the human visual system (HVS). To maximize the watermark strength without introducing visual artifacts, in SS watermarking, the watermark signal is usually modulated by the just-noticeable difference (JND) of the host image. In advanced perceptual models, the JND is characterized as a nonlinear function of local image features. The optimum detection scheme for such nonlinearly embedded watermarks, however, has rarely been studied. In this paper, we address this problem and propose a novel approach that transforms the test signal to a perceptually uniform domain and then performs Bayesian hypothesis testing in that domain. Locally optimum detectors for arbitrary host signal distributions and arbitrary JND models that exploit the self-masking property of the HVS are derived in closed forms, in which the test signal is first nonlinearly preprocessed before a linear correlator is applied. The optimality of the proposed detector is justified mathematically according to the Neyman-Pearson criterion. Simulation results demonstrate the superior performances of the proposed detector over the conventional linear correlation detector.