The upper and lower bounds of the information-hiding capacity of digital images

  • Authors:

  • Fan Zhang;Zhigeng Pan;Kui Cao;Fengbin Zheng;Fangming Wu

  • Affiliations:

  • College of Computer and Information Engineering, Henan University, Kaifeng 475001, PR China;State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027, PR China;College of Computer and Information Engineering, Henan University, Kaifeng 475001, PR China;College of Computer and Information Engineering, Henan University, Kaifeng 475001, PR China;College of Computer and Information Engineering, Henan University, Kaifeng 475001, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

Quantified Score

Hi-index 0.07

Visualization

Abstract

The information-hiding capacity of a digital image is the maximum information that can be hidden in that image, while the lower limit of information hiding is the minimum detectable information capacity. This paper proposes a new method of information-hiding capacity bounds analysis that is based on the neural network theories of attractors and attraction basins. With this method, the processes for determining upper and lower limits of information hiding, are unified within a single theoretical framework. Results of research show that the theory of attraction basins of neural networks can be used to determine the upper limit of information hiding and the theory of attractors of neural networks can be used to determine the lower limit of information hiding.