Papers › Towards Robust Image-in-Audio Deep Steganography
Towards Robust Image-in-Audio Deep Steganography
Jaume Ros, Margarita Geleta, Jordi Pons, Xavier Giro-i-Nieto
The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often present conflicting goals that classical methods have struggled to reconcile. This paper extends and enhances an existing image-in-audio deep steganography method by focusing on improving its robustness. The proposed enhancements include modifications to the loss function, utilization of the Short-Time Fourier Transform (STFT), introduction of redundancy in the encoding process for error correction, and buffering of additional information in the pixel subconvolution operation. The results demonstrate that our approach outperforms the existing method in terms of robustness and perceptual transparency.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Reconstruction | Audio Set | Ours (STFT: magnitude, L1l=1) W-Replicate | SSIM | 0.88 | #1 of 2 | Archive leaderboard | report |
| Image Reconstruction | Audio Set | Ours (STFT: magnitude, L1l=1) WS-Replicate | SSIM | 0.86 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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