Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Fundamentals of wireless communication
Fundamentals of wireless communication
Improved spread spectrum: a new modulation technique for robust watermarking
IEEE Transactions on Signal Processing
Robust optimum detection of transform domain multiplicative watermarks
IEEE Transactions on Signal Processing
Scalar Costa scheme for information embedding
IEEE Transactions on Signal Processing
Informed watermarking by means of orthogonal and quasi-orthogonal dirty paper coding
IEEE Transactions on Signal Processing
An additive approach to transform-domain information hiding andoptimum detection structure
IEEE Transactions on Multimedia
IEEE Transactions on Information Theory
On the achievable throughput of a multiantenna Gaussian broadcast channel
IEEE Transactions on Information Theory
A new decoder for the optimum recovery of nonadditive watermarks
IEEE Transactions on Image Processing
An Enhanced Multiplicative Spread Spectrum Watermarking Scheme
IEEE Transactions on Circuits and Systems for Video Technology
On multiwatermarking in cloud environment
Concurrency and Computation: Practice & Experience
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This paper constructs a class of generalized embeddings of multiplicative watermarks. Ordinary multiplicative and additive methods are included as special cases. The new watermarks automatically adapt to the local contents of host signals, benefiting the perceptual quality. The decoding makes use of the optimal generalized correlation detector. The host interference is precanceled at the embedder side and very high gains are obtained in terms of decoding capability. We develop performance analysis for this new class of embeddings. It turns out that the plain multiplicative watermark is far outperformed by the new embedding. Further, the multiplicative watermark with host interference rejection is still suboptimal. The best embeddings and configurations are specified for typical scenarios. Our construction and performance analyses of the generalized embedding offer a class of new methods. The construction and analyses are confirmed by empirical experiments.