PRoADS: Provably Secure and Robust Audio Diffusion Steganography with latent optimization and backward Euler Inversion
This work addresses secure and robust audio steganography for applications like covert communication, representing a strong specific gain in the field.
The paper tackles the problem of high bit error rates in audio steganography by proposing PRoADS, a framework that embeds secret messages into audio diffusion models, achieving a low BER of 0.15% under MP3 compression.
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.