The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
The role of information theory in watermarking and its application to image watermarking
Signal Processing - Special section on information theoretic aspects of digital watermarking
Attack modelling: towards a second generation watermarking benchmark
Signal Processing - Special section on information theoretic aspects of digital watermarking
Optimal transform domain watermark embedding via linear programming
Signal Processing - Special section on information theoretic aspects of digital watermarking
The Capacity and Attractor Basins of Associative Memory Models
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A framework for evaluating the data-hiding capacity of image sources
IEEE Transactions on Image Processing
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Image watermarking capacity research is to study how much information can be hidden in an image. In watermarking schemes, watermarking can be viewed as a form of communications. Almost all previous works on watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. This paper presents a blind watermarking algorithm using Hopfield neural network, and analyzes watermarking capacity based on neural network for the first time. Result shows that the attraction basin of associative memory decides watermarking capacity.