Speech signal authentication and self-recovery based on DTWT and ADPCM

Abstract

The digital voice is multimedia content of great importance, given the range of applications where it can be found. This paper addresses the shortcomings of existing voice authentication algorithms, presenting a completely blind speech authentication and recovery method based on fragile watermarking using the Least Significant Bit (LSB) method. This scheme obtains a compressed version of the original speech signal by Adaptive Differential Pulse Code Modulation (ADPCM) coding and the Discrete-Time Wavelet Transform (DTWT). Authentication bits are then generated by the SHA256 hash function, and the watermark is afterward embedded in the last three LSBs of the original audio samples. Experimental results evaluated on five different audio databases, each comprising speech signals recorded in different situations, contexts, and languages, have demonstrated a high embedding payload and imperceptibility of the watermark, obtaining an average Signal-to-Noise Ratio (SNR) value above $40dB$. Furthermore, the proposed method demonstrates a strong ability to accurately locate and restore up to 50% of a speech signal that has been tampered with, using no additional information. Moreover, the recovered speech signal is intelligible and has an SNR value higher than other recovery schemes, justifying the efficiency of the proposed method.