A Self-Adaptive Image Steganography Algorithm Based on Cover-Coding and Markov Model

It is a difficulty and hotspot how to desigh steganography algorithms with large-capacity, low-distortion and high statistical security. A self-adaptive image steganography algorithm which takes account of the perceptual distortion and second-order statistical security is proposed. It introduces the smoothness of the various parts of the cover-object to the encoding generation process of cover codes, and reduces the distortion by the reasonable use of a cluster of cover codes in each part of cover-object. In the embedding aspect, in order to improve the statistic security, the algorithm uses a dynamic compensate method based on the image Markov chain model, and it embeds secret information into the least two significant bit (LTSB) planes in order to ensure the capacity. Experiment results show the proposed algorithm has lower distortion and smaller changes of cover statistical distribution than the stochastic LTSB match steganography algorithm and the algorithm which only uses one cover code under the same embedded payload. And the proposed algorithm has larger payloads than one cover code embedding when the distortion and statistical distribution changes are close.