Efficient Audio Steganography Using Generalized Audio Intrinsic Energy With Micro-Amplitude Modification Suppression

• Published in: IEEE transactions on information forensics and security 2024, Vol.19, p.6559-6572
• Main Authors: Su, Wenkang, Ni, Jiangqun, Hu, Xianglei, Li, Bin
• Format: Article
• Language: English
• Subjects: Amplitudes; Audio steganography; Cost function; Costs; Distortion; distortion cost function; Filters; generalized audio intrinsic energy; micro-amplitude suppression; Payloads; Security; Sound filters; Steganography
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2024.3417268

Recent advances in content-adaptive Audio Steganography in Temporal Domain (ASTD) suggest that modification of micro-amplitude samples may compromise its security. To prevent the micro-amplitude samples from being modified, a targeted Large Amplitude First (LAF) rule was adopted in some audio steganographic schemes, e.g., DFR. However, it is observed that the results with LAF rule are often unstable across different datasets, we thus propose a new Micro-Amplitude Suppression (MAS) rule in this paper following the design philosophy of wet paper coding. Unlike DFR where the audio steganographic performance heavily depends on the adopted heuristic filters, we propose to evaluate the embedding cost of cover audio with the Generalized Audio Intrinsic Energy (GAIE), which is obtained by calculating the weighted sum of squared DCT coefficients for each segmented audio clip with carefully designed weights. Extensive experimental results demonstrate that the proposed MAS rule tends to be more general and consistent than the LAF rule, and the proposed GAIE also shows better empirical security performance and audio quality compared to the advanced AAC and DFR_res (a variant of DFR). In addition, by preventing the micro-amplitude samples from being modified, the proposed GAIE_MAS can not only outperform other hand-crafted audio steganographic schemes but also the recently emerged deep learning-based schemes, e.g., IAA.