Thanks to advanced audio editing software, speech recordings can be tampered with very easily. In case the speech recordings are used as forensic evidence, adding the audio recordings together, cutting them, and changing their content are legally unacceptable and constitute a crime. Audio copy-move forgery is the most common forgery for the purpose of changing the content of the speech. Audio copy-move forgery is performed by copying a segment in the audio and pasting it anywhere in the same audio. In this study, a robust and new method based on cochleagram images is proposed to detect audio copy-move forgery. The proposed method uses cochleagram images of the voiced parts of the audio to detect forgery clues in the input audio file. For this purpose, the audio file is first split into voiced parts using a pitch-based Voice Activity Detection (VAD) method. Each audio part is then converted into a cochleagram image. Structural similarity index measure (SSIM) is used to calculate the similarity between cochleagram images. After calculating the SSIM values between the cochleagram images, the proposed forgery localization algorithm is performed. In this algorithm, the SSIM values among the cochleagram images are first sorted in descending order. The length ratio between these pairs of segments is calculated in order to determine which of the values in this descending order are duplicated segment pairs. If this ratio exceeds the specified percentage rate, these segment pairs are marked as forged segments. Finally, the proposed audio copy-move forgery detection method is evaluated against the state-of-the-art approaches with two Copy-Move Forgery Detection (CMFD) database and forged databases created from TIMIT and the Arabic Speech Corpus database. The experimental results show that the proposed method is significantly high robust against post-processing operations compared to other studies.

The rapid advancement of digital environments has led to an increase in multimedia forgery, particularly in the realm of audio, which leads to significant threats to the reliability of digital evidence. This paper presents a novel method to detect audio copy-move forgery, a type of manipulation where segments of an audio file are duplicated and moved to different locations within the same file. The proposed method consists of two main stages. In the first stage, the frequency range containing the forged segments is identified by extracting high-resolution spectrograms from the audio and matching keypoints within the spectrogram images to detect duplicated segments. The frequency range of the sub-spectrogram images with the highest match density is considered the location of the repeated segments. A swarm-based optimization approach is used to adaptively determine this dense region. The audio is then bandpass filtered using the identified frequency range, and the second stage begins. In this stage, the filtered audio is represented as a graph using the proposed spiral pattern information extraction method. Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. It provides a robust solution for verifying audio authenticity, even under various additional attack scenarios, and shows potential for generalization.