Detecting audio copy-move forgery with an artificial neural network

Akdeniz, Fulya; Becerikli, Yaşar

doi:10.1007/s11760-023-02856-w

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…The experimental results are presented in Table 2. Existing deep learning-based methods for audio copy-move forgery detection are all based on binary classification of audio, including the ANN method proposed by Akdeniz et al [16] and the CNN method proposed by Ustubiol et al [17]. The results of these two methods are also listed in Table 2.…”

Section: Binary Classification Of Audio Copy-move Forgerymentioning

confidence: 99%

“…However, the development of audio forensics based on deep learning has been relatively slow, and there are relatively few methods for detecting audio copy-move forgery. Akdeniz et al [16] used an artificial neural network (ANN) to achieve binary classification by learning Mel frequency cepstral coefficients and linear prediction coefficients. Ustubiol et al [17] used a classical convolutional neural network (CNN) to extract audio spectrum features and then performed binary classification through fully connected layers.…”

Section: Introductionmentioning

confidence: 99%

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An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis

Zhao,

Zhang,

Wang

et al. 2024

Applied Sciences

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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0–6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%.

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Section: Binary Classification Of Audio Copy-move Forgerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis

Zhao,

Zhang,

Wang

et al. 2024

Applied Sciences

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Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

Akdeniz,

Becerikli

2024

J Supercomput

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One of the most pressing challenges in audio forgery detection—a major topic of signal analysis and digital forensics research—is detecting copy-move forgery in audio data. Because audio data are used in numerous sectors, including security, but increasingly tampered with and manipulated, studies dedicated to detecting forgery and verifying voice data have intensified in recent years. In our study, 2189 fake audio files were produced from 2189 audio recordings on the TIMIT corpus, for a total of 4378 audio files. After the 4378 files were preprocessed to detect silent and unsilent regions in the signals, a Mel-frequency-based hybrid feature data set was obtained from the 4378 files. Next, RNN and LSTM deep learning models were applied to detect audio forgery in the data set in four experimental setups—two with RNN and two with LSTM—using the AdaGrad and AdaDelta optimizer algorithms to identify the optimum solution in the unlinear systems and minimize the loss rate. When the experimental results were compared, the accuracy rate of detecting forgery in the hybrid feature data was 76.03%, and the hybrid model, in which the features are used together, demonstrated high accuracy even with small batch sizes. This article thus reports the first-ever use of RNN and LSTM deep learning models to detect audio copy-move forgery. Moreover, because the proposed method does not require adjusting threshold values, the resulting system is more robust than other systems described in the literature.

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