43 research outputs found
Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking
In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection
Optimal Watermark Embedding and Detection Strategies Under Limited Detection Resources
An information-theoretic approach is proposed to watermark embedding and
detection under limited detector resources. First, we consider the attack-free
scenario under which asymptotically optimal decision regions in the
Neyman-Pearson sense are proposed, along with the optimal embedding rule.
Later, we explore the case of zero-mean i.i.d. Gaussian covertext distribution
with unknown variance under the attack-free scenario. For this case, we propose
a lower bound on the exponential decay rate of the false-negative probability
and prove that the optimal embedding and detecting strategy is superior to the
customary linear, additive embedding strategy in the exponential sense.
Finally, these results are extended to the case of memoryless attacks and
general worst case attacks. Optimal decision regions and embedding rules are
offered, and the worst attack channel is identified.Comment: 36 pages, 5 figures. Revised version. Submitted to IEEE Transactions
on Information Theor