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.

中文翻译：

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使用具有微幅度修改抑制的广义音频固有能量的高效音频隐写术


时域内容自适应音频隐写术 (ASTD) 的最新进展表明，微幅度样本的修改可能会损害其安全性。为了防止微幅度样本被修改，一些音频隐写方案（例如 DFR）中采用了有针对性的大幅度优先（LAF）规则。然而，据观察，LAF规则的结果在不同的数据集上往往不稳定，因此我们在本文中遵循湿纸编码的设计理念提出了一种新的微振幅抑制（MAS）规则。与 DFR 不同，DFR 的音频隐写性能很大程度上取决于所采用的启发式滤波器，我们建议使用广义音频固有能量（GAIE）来评估覆盖音频的嵌入成本，GAIE 是通过计算每个分段的 DCT 系数平方的加权和而获得的。具有精心设计权重的音频剪辑。大量的实验结果表明，所提出的 MAS 规则往往比 LAF 规则更加通用和一致，并且与先进的 AAC 和 DFR_res（DFR 的一种变体）相比，所提出的 GAIE 还表现出更好的经验安全性能和音频质量。此外，通过防止微幅度样本被修改，所提出的 GAIE_MAS 不仅可以优于其他手工制作的音频隐写方案，而且可以优于最近出现的基于深度学习的方案，例如 IAA。