Constructing Immunized Stego-Image for Secure Steganography via Artificial Immune System

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.