Image watermarking capacity analysis using hopfield neural network

Authors:
Fan Zhang;Hongbin Zhang
Affiliations:
Institute of Computer, Beijing University of Technology, Beijing, China;Institute of Computer, Beijing University of Technology, Beijing, China
Venue:
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
Year:
2004

Citing 4
Cited 0

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Attack modelling: towards a second generation watermarking benchmark

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Optimal transform domain watermark embedding via linear programming

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A framework for evaluating the data-hiding capacity of image sources

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Abstract

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 communication and image can be considered as a communication channel to transmit messages. 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 analyze watermarking capacity based on neural network. Result shows that the attraction basin of associative memory decides watermarking capacity.