The maximum capacity and minimum detectable capacity of information hiding in digital images

Authors:
Fan Zhang;Xianxing Liu;Jie Li;Xinhong Zhang
Affiliations:
College of Computer & Information Engineering, Henan University, Kaifeng, P.R. China;College of Computer & Information Engineering, Henan University, Kaifeng, P.R. China;College of Computer & Information Engineering, Henan University, Kaifeng, P.R. China;Department of Computer Center, Henan University, Kaifeng, P.R. China
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Year:
2006

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Abstract

Information hiding capacity of digital image is the maximum information that can be hidden in an image. But the lower limit of information hiding, the minimum detectable information capacity is also an interesting problem. This paper proposes new method of the information hiding capacity bounds analysis that is based on the theories of attractors and attraction basin of neural network. The upper limit and lower limit of information hiding, namely the maximum information capacity and the minimum detectable information capacity are unified in a same theory frame. The results of research show that the attraction basin of neural network decides the upper limit of information hiding, and the attractors of neural network decide the lower limit of information hiding.