International Journal of Knowledge-based and Intelligent Engineering Systems
EURASIP Journal on Applied Signal Processing
Machine learning based adaptive watermark decoding in view of anticipated attack
Pattern Recognition
A lightweight rao-cauchy detector for additive watermarking in the dwt-domain
Proceedings of the 10th ACM workshop on Multimedia and security
IEEE Transactions on Image Processing
Human motion analysis via statistical motion processing and sequential change detection
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Watermark detection on quantized transform coefficients using product bernoulli distributions
Proceedings of the 12th ACM workshop on Multimedia and security
Transform based additive data hiding based on a hierarchical prior
ECC'11 Proceedings of the 5th European conference on European computing conference
Robust copyright marking using Weibull distribution
Computers and Electrical Engineering
Detection for multiplicative watermarking in DCT domain by Cauchy model
ICICS'11 Proceedings of the 13th international conference on Information and communications security
Embedding image watermarks into local linear singularity coefficients in ridgelet domain
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
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This paper addresses issues that arise in copyright protection systems of digital images, which employ blind watermark verification structures in the discrete cosine transform (DCT) domain. First, we observe that statistical distributions with heavy algebraic tails, such as the alpha-stable family, are in many cases more accurate modeling tools for the DCT coefficients of JPEG-analyzed images than families with exponential tails such as the generalized Gaussian. Motivated by our modeling results, we then design a new processor for blind watermark detection using the Cauchy member of the alpha-stable family. The Cauchy distribution is chosen because it is the only non-Gaussian symmetric alpha-stable distribution that exists in closed form and also because it leads to the design of a nearly optimum detector with robust detection performance. We analyze the performance of the new detector in terms of the associated probabilities of detection and false alarm and we compare it to the performance of the generalized Gaussian detector by performing experiments with various test images.