Estimating Channel Knowledge for Robust Steganography

Steganography is a technique for embedding secret messages into digital media while preserving perceptual integrity and avoiding detection. Robust steganography specifically enables secure communication through lossy channels with disturbances, where a critical challenge lies in the receiver's ability to accurately reconstruct hidden messages from corrupted stego data-a process termed error-correction. Recent advancements in stego coding schemes for robust steganography leverage probabilistic decoding to enhance message error-correction accuracy. These schemes employ Maximum-A-Posteriori (MAP) decoders that integrate channel knowledge, i.e. the probability distribution of stego symbols derived from received data, thus necessitating precise estimation of such channel knowledge. While existing approach for Channel Knowledge Estimation (CKE) relies on heuristic, handcrafted methods grounded in empirical assumptions, its effectiveness is limited by the inherent complexity of media content and the diversity of channel disturbance. Besides, it is not compatible with some of the embedding method for robust steganography. To address these limitations, we propose a deep learning-based framework for universal channel knowledge estimation, which significantly improves error-correction performance. Furthermore, we extend this framework to multi-channels robust steganography scenarios, formulating a novel multi-channels knowledge estimation paradigm to enhance message correctness through transmission in multiple channels. Experimental results on various state-of-the-art robust steganography methods demonstrate that our approach outperforms existing CKE methods and coding schemes.