Detection of speech tampering using sparse representations and spectral manipulations based information hiding

Speech tampering has brought serious problems to speech security. Information hiding method can be used for tampering detection if it can satisfy several competitive requirements, e.g., inaudibility, robustness, blindness, and fragility. According to preliminary analysis, spectral envelope and formants are important indicators of tampering, since tampering the speech will unavoidably modify the shape of the spectral envelope and the locations/magnitudes of the formants. By taking advantage of this, this paper proposes a spectral manipulations based information hiding method for tampering detection in sparse domain. To robustly extract the embedded information, the Robust Principal Component Analysis (RPCA) is employed to decompose the original speech into sparse component and low-rank component. The sparse component which contains the main spectral envelope and formant structure is selected for information hiding/embedding via spectral manipulations, by controlling the shape and power of formants with line spectral frequencies (LSFs). Evaluation results suggest that the proposed method can satisfy inaudibility, robustness, and fragility. Furthermore, it is able to detect both the temporal tampering and acoustic feature based tampering with reasonable accuracy.