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Author

Ming-Yu Liu

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Open access Sep 2026

Cost-Guided Joint Mask-Perturbation Optimization with Attentive Decoding for Image Steganography

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.

Qiu-Ping Li, Xing-Yu Chen, Hai-Xia Wang et al. · 0 citations

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