PRoADS: Provably Secure and Robust Audio Diffusion Steganography with latent optimization and backward Euler Inversion
On the map: Diffusion models → Robustness, privacy and adversarial aspects
Abstract (arXiv)
This paper proposes PRoADS, a provably secure and robust audio steganographic framework based on audio diffusion models. As a generative steganography scheme, PRoADS embeds secret messages into the initial noise of diffusion models via orthogonal matrix projection. To address the reconstruction errors in diffusion inversion that cause high bit error rates (BER), we introduce Latent Optimization and Backward Euler Inversion to minimize the latent reconstruction and diffusion inversion errors. Comprehensive experiments demonstrate that our scheme sustains a remarkably low BER of 0.15% under 64 kbps MP3 compression, significantly outperforming existing methods and exhibiting strong robustness.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 10 of its 20 references that are in the map. First posted on arXiv 11 Mar 2026.
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10 of 20 references.
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Same direction, newest first
- 2024Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models7 Apr
- 2023Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection30 May
- 2023CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography26 May
- 2023Generative Steganography Diffusion5 May
Other references · 6
- 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
- 2023AudioLDM: Text-to-Audio Generation with Latent Diffusion Modelscited by 592
- 2024Fast Timing-Conditioned Latent Audio Diffusioncited by 178
- 2023MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategiescited by 94
- 2024EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformercited by 30
- 2022Generative Steganography Networkcited by 15
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