NACA: A Joint Distortion-Based Non-Additive Cost Assignment Method for Video Steganography
Yi Chen, Zoran A. Salcic, Hongxia Wang, Kim‐Kwang Raymond Choo, Xuyun Zhang · IEEE Transactions on Dependable and Secure Computing · 2022
Lots of non-additive cost assignment methods designed for image steganography have improved the security of stego images, but surprisingly there are only a few such non-additive cost assignment methods for video steganography. In this paper, we first analyze the distortion propagation by decomposing it into inner-block, inter-block, and inter-frame distortion drifts. Then, we determine the inner-block distortion drift (caused by the embedding modifications) that induces the inter-block and the inter-frame distortion drifts, using prediction. Based on the findings, we compose a joint distortion for all transform coefficients in each transform block. Finally, we propose a joint distortion-based non-additive cost assignment (NACA) method to reduce the inner-block distortion drift by distortion compensation. This allows us to further reduce both intra-frame (inter-block) and inter-frame distortion drifts, and achieve enhanced security. We conduct extensive experiments to evaluate the performance of NACA, in terms of security and coding performance. The evaluation results demonstrate that NACA achieves improved security and visual stego video quality, and maintains a very marginal increase in bit-rate, in comparison to four other competing additive cost assignment approaches.