PRoADS: Provably Secure and Robust Audio Diffusion Steganography with latent optimization and backward Euler Inversion

TL;DR

PRoADS introduces a secure and robust audio steganography framework leveraging diffusion models, orthogonal projection, and advanced inversion techniques to achieve low error rates and withstand compression attacks.

Contribution

It presents a novel steganography scheme with provable security and robustness, utilizing latent optimization and backward Euler inversion to improve diffusion inversion accuracy.

Findings

Achieves a BER of 0.15% under 64 kbps MP3 compression.

Outperforms existing methods in robustness and security.

Demonstrates effectiveness through comprehensive experiments.

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.