![]() The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature.Īlcantarilla PF, Solutions T (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. ![]() The proposed approach to eliminate false matches evaluates the correctness of the matches. The ordering points to identify the clustering structure method (OPTICS) is used by the approach to match the corresponding descriptors. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Malicious individuals can modify speech recordings with advanced audio editing software to create forged audio.
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