Anti-Forensics of Audio Source Identification Using Generative Adversarial Network

Digital audio recording is the main evidence used in the field of judicial forensics. Recently, a number of digital audio forensic techniques have been developed and the audio source identification (ASI) is one of the most active research topics. Most of existing ASI works mainly focus on improving the performance of detection accuracy and robustness. Little consideration has been given to ASI anti-forensics, which aims at attacking the forensic techniques. To expose the weaknesses of these source identification methods, we propose an anti-forensic framework based on generative adversarial network (GAN) to falsify the source information of an audio by adding specific disturbance. The experimental results show that the falsified audio can deceive the forensic methods effectively, and can even control their conclusions. Three state-of-art ASI methods have been evaluated as the attacking targets. For the confusing attack, the proposed method can significantly reduce their detection accuracies from about 97 to less than 5. For the misleading attack, a misleading rate about 81.32 has been achieved while ensuring the perceptual quality of the anti-forensic audio.