Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion

One of the most common forms of audio forgery is copying and moving certain audible segments of audio to other locations in the same audio. The audio features of the pasted regions in such audio forgeries become very dissimilar to the audio features of the copied segments after post-processing. This dissimilarity makes detecting such tampering a major challenge. To address this problem, we propose a robust audio copy-move forgery detection method using a Decreasing Convolutional Kernel Neural Network (DCKNN), data augmentation, and digital fusion. In the proposed algorithm, Mel spectrogram and Hilbert–Huang spectrogram of the audio are extracted, and then they are fused by weighting coefficients, which are gained through extensive experiments. New spectrogram images are generated by weighted fusion, and these spectrogram images are used to train the proposed DCKNN model. The trained DCKNN can effectively detect copy-move forgery. The DCKNN model consists of a combination of four convolutional groups, each with different sensitivities to the two audio categories. We solve the problem of different sensitivities by sequentially lowering the parameters of the convolutional layers in the four convolutional groups, thus obtaining high accuracy in audio classification. The experimental results show that the proposed scheme is robust to most typical post-processing operations, including additive noise, compression, median filtering, resampling, re-quantization, and low-pass filtering, etc al. In addition, our method shows better performance in the detection of forged audio with multiple attacks. Compared to the state-of-the-art algorithms, the proposed algorithm has advantages in terms of accuracy, precision, and F1 score.