Existing audio watermarking methods usually treat the host audio signals of a function of time or frequency individually, while considering them in the joint time-frequency (TF) domain has received less attention. This paper proposes an audio watermarking framework from the perspective of TF analysis. The proposed framework treats the host audio signal in the 2-dimensional (2D) TF plane, and selects a series of patches within the 2D TF image. These patches correspond to the TF clusters with minimum averaged energy, and are used to form the feature vectors for watermark embedding. Classical spread spectrum embedding schemes are incorporated in the framework. The feature patches that carry the watermarks only occupy a few TF regions of the host audio signal, thus leading to improved imperceptibility property. In addition, since the feature patches contain a neighborhood area of TF representation of audio samples, the correlations among the samples within a single patch could be exploited for improved robustness against a series of processing attacks. Extensive experiments are carried out to illustrate the effectiveness of the proposed system, as compared to its counterpart systems. The aim of this work is to shed some light on the notion of audio watermarking in TF feature domain, which may potentially lead us to more robust watermarking solutions against malicious attacks.
Robust Audio Watermarking Against the D/A and A/D conversions
音频水印技术在多媒体信息安全中发挥着重要的作用。在许多音频水印应用中，经常涉及到D/A和A/D转换（本文用DA/AD表示）。然而，在以前的作品中，音频水印对DA/AD转换的鲁棒性问题尚未引起足够的重视。在我们广泛的调查中，已经发现，由DA/AD转换引起的水印音频信号的劣化主要表现在波形幅度失真和线性时间缩放方面，使得水印提取失败。因此，本文提出了一种基于小波变换的音频水印算法，该算法对DA/AD转换具有鲁棒性。为了抵抗幅度失真，该算法利用低频子带内不同DWT系数组之间的相对能量关系，通过自适应控制嵌入强度来嵌入水印。此外，该算法还设计了一种处理线性时间尺度的方法。利用小波变换的时频局部化特性，减少了小波变换的计算量。实验结果表明，该音频水印算法对DA/AD转换、其他常见的音频处理操作以及StirMark Benchmark for Audio中的攻击具有较好的鲁棒性。
Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Key Laboratory of Intelligent Media and Content Understanding（深圳市智能媒体与内容理解重点实验室）；Tencent AI Lab（腾讯人工智能实验室）
