Robust audio copy-move detection and localisation based on Smoothed Pseudo Wigner-Ville Distribution and adaptive processing

Audio Copy-Move forgery is a copy and paste operation within the same audio source that alters the original audio content in order to deceive or mislead listeners. Existing audio copy-move forgery detection algorithms show good robustness against audio that has undergone a single post-processing (noise addition, compression, median filtering, etc.), but perform poorly in detecting copy-move that have undergone a mixed post-processing attack, where multiple post-processes are present in the audio simultaneously. To overcome this problem, we propose a new adaptive processing method based on Variational Modal Decomposition and Minimum Redundancy Maximum Relevance. First, the forged audio is decomposed into multiple Intrinsic Mode Functions located in different frequency domains. Scores are then computed based on correlation and redundancy. The calculated scores are used as weights and reconstructed by weighting to obtain the final audio signal. Through feature selection and weighted reconstruction, the new audio signal is provided with adaptive attributes. We chose the Smoothed Pseudo Wigner-Weil Distribution as the feature for performing the final copy-move detection. For the experimental part, we created hybrid post-processing datasets and applied adaptive processing to existing representative copy-move detection methods, all of which have improved detection results. Finally, we tested the robustness of the proposed method and the performance is optimal in comparison with other methods. The experimental results show that the proposed method is robust to both single and hybrid post-processing attacks.