Abstract
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.
Abstract (translated)
本文提出了PRoADS，这是一种基于音频扩散模型的可证明安全且鲁棒的音频隐写框架。作为一种生成式隐写方案，PRoADS通过正交矩阵投影将秘密消息嵌入到扩散模型的初始噪声中。为了应对在扩散逆向过程中导致高比特误码率（BER）的重建误差问题，我们引入了潜在优化和反向欧拉逆向技术来最小化潜伏态重建和扩散逆过程中的错误。全面的实验表明，在64kbps MP3压缩条件下，我们的方案能够保持极低的0.15%比特误码率，这显著优于现有的方法，并表现出强大的鲁棒性。
URL
https://arxiv.org/abs/2603.10314
