Cost-Guided Joint Mask-Perturbation Optimization with Attentive Decoding for Image Steganography
TL;DR
This paper proposes a cost-guided joint mask–perturbation optimization with attentive decoding for image steganography method (CMAD), which jointly optimizes the embedding mask, perturbation magnitude, and decoding-network parameters, thereby improving recovery accuracy while preserving imperceptibility.
Abstract
Digital image steganography aims to imperceptibly embed secret information into a cover image to enable covert communication. This paper focuses on image-level imperceptibility and recovery quality, and proposes a cost-guided joint mask–perturbation optimization with attentive decoding for image steganography method (CMAD). In an end-to-end differentiable framework, CMAD jointly optimizes the embedding mask, perturbation magnitude, and decoding-network parameters, thereby improving recovery accuracy while preserving imperceptibility. During optimization, the proposed AniCost cost-map guidance mechanism computes pixel-level embedding costs through wavelet-based anisotropy analysis, and uses a probability-map guidance loss to directly encourage the mask to activate in complex-texture regions and deactivate in smooth regions. The channel-attention-based decoding network is fine-tuned for each image pair during optimization to adapt to the current pair. Experimental results show that the stego images generated by CMAD achieve PSNR values of 55–59 dB, while the recovered secret images achieve PSNR values of 35–40 dB.