A Novel Integrity Authentication Algorithm Based on Perceptual Speech Hash and Learned Dictionaries

Perceptual speech hash and robust watermarking have been widely investigated to solve the problems of authenticating speech integrity. The former generates a watermark and the latter embeds the watermark into the speech signal to implement speech integrity authentication. In this paper, we propose a perceptual speech hash algorithm and a robust watermarking algorithm for speech integrity authentication. To obtain perceptual speech hash values, we propose a gammatone filter model of the speech signal to extract sensitive auditory features (denoted by gammatone features). A random Gaussian matrix is used to reduce the dimensionality of the features of the gammatone to generate perceptual speech hash values. For the watermarking algorithm, we construct learned dictionaries to obtain the robust sparse feature of coefficients of the stationary wavelet transforms, and embed a watermark (perceptual speech hash values) into the sparse feature by patchwork and quantization index modulation. We illustrate the good imperceptibility of the authentication scheme in terms of the signal-to-noise ratio, objective difference grade, and subjective difference grade, and verify its robustness against common signal processing operations while maintaining imperceptibility. Moreover, our proposed method is sensitive to the malicious modification of the watermarked speech. Compared with state-of-the-art algorithms, the proposed algorithm can obtain better comprehensive performance in the detection and localization of tampering with the content of speech.