Spatial Image Steganography in Matlab

Abstract:

Most of the recently proposed steganographic schemes are based on minimizing an additive distortion function defined as the sum of embedding costs for individual pixels. In such an approach, mutual embedding impacts are often ignored. In this paper, we present an approach that can exploit the interactions among embedding changes in order to reduce the risk of detection by steganalysis. It employs a novel strategy, called clustering modification directions (CMD), based on the assumption that when embedding modifications in heavily textured regions are locally heading towards the same direction, the steganographic security might be improved. To implement the strategy, a cover image is decomposed into several sub-images, in which message segments are embedded with well-known schemes using additive distortion functions. The costs of pixels are updated dynamically to take mutual embedding impacts into account. Specifically, when neighboring pixels are changed towards a positive/negative direction, the cost of the considered pixel is biased towards the same direction. Experimental results show that our proposed CMD strategy, incorporated into existing steganographic schemes, can effectively overcome the challenges posed by the modern steganalyzers with high-dimensional features.