A Robust Coverless Audio Steganography Based on Differential Privacy Clustering

作者：Yan Feng, Longting Xu, Xiaochen Lu, Guanglin Zhang, Wei Rao · 发表于：IEEE Transactions on Multimedia · 年份：2025 · DOI：10.1109/tmm.2025.3543107 · 被引用次数：4 · 研究领域：Advanced Steganography and Watermarking Techniques、Digital Media Forensic Detection、Chaos-based Image/Signal Encryption

Conventional audio steganography methods typically require embedding secret information into the carrier, making them vulnerable to steganalysis. To address this issue, we propose a novel coverless audio steganography method that hides information by generating carriers and establishing mapping rules rather than embedding data directly. Our approach leverages a differential privacy clustering algorithm to cluster audio data and select representative audio files, thereby enhancing the security of the steganography. Additionally, we introduce an improved audio feature extraction method that combines traditional Mel-frequency cepstral coefficients (MFCC) with global statistical information, significantly boosting the robustness of the secret information against common audio attacks, particularly time-stretching attacks. Experimental results show that our method achieves a robustness rate of up to 95% against time-stretching and maintains an average security accuracy rate exceeding 97% across various attack scenarios. The proposed method ensures that the audio carrier remains unaltered, thus effectively resisting detection by steganalysis tools. This innovative approach provides a practical and efficient solution for the secure transmission of information in the digital era.