Anil Kumar Goteti

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Quantization Index Modulation (QIM) methods are widely used for blind data embedding and watermarking. Given a QIM watermarking code, we ask what is the attacker's noise distribution that maximizes probability of error of the detector. For memoryless attacks, the problem is reduced to a convex programming problem. Next, we derive QIM code parameters that(More)
The problem of blind watermarking of an arbitrary host signal in /spl Ropf//sup n/ under squared-error distortion constraints and Gaussian attacks is considered in this paper. While distortion-compensated lattice quantization index modulation (QIM), using nearly spherical Voronoi cells, is known to be asymptotically capacity-achieving in this setup, our(More)
This paper examines the role of attacker's memory in quantization index modulation (QIM) watermarking systems. First we derive the attacker's noise distribution that maximizes probability of error of the detector. Next, we derive QIM code parameters that are minmax optimal. The minmax optimal embedding strategy involves randomized lattice rotations, and the(More)
While binning is a fundamental approach to blind data embedding and watermarking, an attacker may devise various strategies to reduce the effectiveness of practical binning schemes. The problem analyzed in this paper is design of worst-case noise distributions against L-dimensional lattice quantization index modulation (QIM) watermarking codes. The cost(More)
Perceptual watermarking methods are designed to be transparent and robust to attacks. A perceptual model based on just noticeable difference levels introduces amplitude constraints on the watermark and the noise generated by an attacker. Two problems are considered: (1) detection performance for embedding a single bit in n data; (2) Shannon capacity. In(More)
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