Triple-Stage Robust Audio Steganography Framework with AAC Encoding for Lossy Social Media Channels
Ziping Zhang, Jiamin Zeng, Yan Xu, Xiaowei Yi, Yun Cao, Changjun Liu · 2025
Robust audio steganography has significant application value for secure communication, especially with the rise of social media platforms.However, the complexity of audio encoding and the distortions introduced by lossy channels have hindered the research in this field.This paper systematically analyzes the origins of this challenge, and evaluates the limitations of previous methods.Building on this foundation, we propose a triple-stage Robust Audio Steganography Framework (RASF), specifically designed for AAC encoding process.RASF consists three essential stages: Psy-Window Control to synchronize psychoacoustic model parameters, Robust Embedding Domain Construction to establish a robust embedding domain using stable quantized coefficients, and Error Correction to ensure reliable data recovery.Experiments demonstrate that the proposed framework achieves high capacity and strong robustness against compression.Notably, tests conducted on social media platforms reveal a very low bit error rate, enabling zero-bit-error transmission when combined with error-correcting codes.RASF addresses critical gaps in robust audio steganography, offering a practical solution for covert communication over lossy social media channels.