A semi fragile watermarking algorithm based on compressed sensing applied for audio tampering detection and recovery

Research on audio tampering detection and recovery plays an important role in the field of audio integrity, and authenticity certification. Generally, we use technology of fragile/semi fragile watermarking to detect and recover tampered audio. In this study, a new scheme for watermark embedding, tampering detection, and recovery is proposed. In the new scheme, we get the compressed version of original audio signal using compressed sensing technology and apply discrete wavelet transform (DWT) to each audio frame. In process of embedding, a new self-adaptive algorithm is proposed. Watermark is the quantized reference value of original framed audio signal and tampering location data, and is embedded in the region with low energy of high frequency coefficients and high energy of low frequency coefficients respectively after 2-level DWT. In process of detection, we locate tampered areas by comparing the value of generated random number and extracted watermark after XOR operation with the extracted location data. As for speech, we set a threshold to judge whether it is tampered or not. At last, we extract watermark in areas which are not damaged and get the recovered signal after decompression. Experiments and analysis show that signal after embedding has at least 5 dB higher average signal-to-noise ratio than others, and broken frames and groups can be detected exactly. When signal is destroyed by 20%, 98% of the corpus is intelligible after recovery, and even destroyed by 50%, 80% of the corpus recovered is also intelligible. Compared with other recovery algorithms, audio signal recovered by our proposal has a higher signal-to-noise ratio and a better robustness to some signal processing. When tampering rate is 50%, the average detection rate is over 93%, which indicates that our method is workable.