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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhang, F., Zhang, H. (2004). Image Watermarking Capacity Analysis Using Hopfield Neural Network. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_94
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DOI: https://doi.org/10.1007/978-3-540-30543-9_94
Publisher Name: Springer, Berlin, Heidelberg
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