A Framework of Enhancing Image Steganography With Picture Quality Optimization and Anti-Steganalysis Based on Simulated Annealing Algorithm

Picture quality and statistical undetectability are two key issues related to steganography techniques. In this paper, we propose a closed-loop computing framework that iteratively searches proper modifications of pixels/coefficients to enhance a base steganographic scheme with optimized picture quality and higher anti-steganalysis capability. To achieve this goal, an anti-steganalysis tester and an embedding controller-based on the simulated annealing (SA) algorithm with a proper cost function-are incorporated into the processing loop to conduct the convergence of searches. The cost function integrates several performance indices, namely, the mean square error, the human visual system (HVS) deviation, and the differences in statistical features, and guides a proper direction of searches during SA optimization. Our proposed framework is suitable for the kind of steganographic schemes that spreads each message information into multiple pixels/coefficients. We have selected two base steganographic schemes for implementation to show the applicability of the proposed framework. Experiment results show that the base schemes can be enhanced with better performances in image PSNR (by more than 5.0 dB), file-size variation, and anti-steganalysis pass-rate (by about 10% ~ 86%, at middle to high embedding capacities).