Anti-Forensics of Environmental-Signature-Based Audio Splicing Detection and Its Countermeasure via Rich-Features Classification

Numerous methods for detecting audio splicing have been proposed. Environmental-signature-based methods are considered to be the most effective forgery detection methods. The performance of existing audio forensic analysis methods is generally measured in the absence of any anti-forensic attack. Effectiveness of these methods in the presence of anti-forensic attacks is therefore unknown. In this paper, we propose an effective anti-forensic attack for environmental-signature-based splicing detection method and countermeasures to detect the presence of the anti-forensic attack. For anti-forensic attack, dereverberation-based processing is proposed. Three dereverberation methods are considered to tamper with the acoustic environment signature. Experimental results indicate that the proposed dereverberation-based anti-forensic attack significantly degrades the performance of the selected splicing detection method. The proposed countermeasures exploit artifacts introduced by the anti-forensic processing. To detect the presence of potential anti-forensic processing, a machine learning-based framework is proposed. In particular, the proposed anti-forensic detection method uses a rich-feature model consisting of Fourier coefficients, spectral properties, high-order statistics of musical noise residuals, and modulation spectral coefficients to capture traces of dereverberation attacks. The performance of the proposed framework is evaluated on both synthetic data and real-world speech recordings. The experimental results show that the proposed rich-feature model can detect the presence of anti-forensic processing with an average accuracy of 95%.