Security Measure for Image Steganography Based on High Dimensional KL Divergence

Abstract

Steganographic security is the research focus of steganography. Current steganography research emphasizes on the design of steganography algorithms, but the theoretical research about steganographic security measure is relatively lagging. This paper proposes a feasible image steganographic security measure based on high dimensional KL divergence. It is proved that steganographic security measure of higher dimensional KL divergence is more accurate. The correlation between neighborhood pixels is analyzed from the principle in imaging process and content characteristics, and it is concluded that 9-dimensional probability statistics are effective enough to be used as steganographic security measure. Then in order to reduce the computational complexity of high dimensional probability statistics and improve the feasibility of the security measure method, a security measure dimension reduction scheme is proposed by applying gradient to describe image textures. Experiments show that the proposed steganographic security measure method is feasible and effective and more accurate than measure method based on 4-dimensional probability statistics.