Audio Deepfake Detection Using Deep Learning
ABSTRACT This study introduces an enhanced Siamese convolutional neural network (Siamese CNN) architecture with a novel StacLoss function and self‐attention modules for efficient identification of audio deepfakes. Our module directly compares unprocessed original audio with modified audio by initial...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70087 |
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| author | Ousama A. Shaaban Remzi Yildirim |
| author_facet | Ousama A. Shaaban Remzi Yildirim |
| author_sort | Ousama A. Shaaban |
| collection | DOAJ |
| description | ABSTRACT This study introduces an enhanced Siamese convolutional neural network (Siamese CNN) architecture with a novel StacLoss function and self‐attention modules for efficient identification of audio deepfakes. Our module directly compares unprocessed original audio with modified audio by initially applying convolutional operations and dual branches to extract complex characteristics from raw audio signals. These operations are followed by residual connections, which enhance the network's performance. The self‐attention modules are trained in a layered way alongside these fundamental layers to detect multi‐headed attention within audio frames. The StacLoss output represents a customized version of the contrastive loss function. It aids the network in distinguishing between original and modified audios by minimizing the loss between pairs of original audio that have the same identity while maximizing the distance between manipulated audio samples and enhances the process of extracting features compared to standard techniques. The efficacy of the method has been verified by examining a range of audio modifications, and its resilience has been thoroughly assessed on the ASVspoof2019 dataset by comprehensive testing across all possible audio manipulation situations. The proposed Siamese convolutional neural network (CNN) outperformed both machine and deep learning models, achieving impressive metrics. It achieved a remarkable accuracy of 98%, precision of 97%, recall of 96%, F1 score of 96.5%, ROC‐AUC of 99%, and an equal error rate (EER) of 2.95%. |
| format | Article |
| id | doaj-art-8b87628ec7084a5baf22d959c140c71a |
| institution | DOAJ |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-8b87628ec7084a5baf22d959c140c71a2025-08-20T02:54:02ZengWileyEngineering Reports2577-81962025-03-0173n/an/a10.1002/eng2.70087Audio Deepfake Detection Using Deep LearningOusama A. Shaaban0Remzi Yildirim1Graduate School of Natural and Applied Sciences Ankara Yıldırım Beyazıt University Ankara TurkeyDepartment of Computer Engineering Tokat Gaziosmanpaşa University Tokat TurkeyABSTRACT This study introduces an enhanced Siamese convolutional neural network (Siamese CNN) architecture with a novel StacLoss function and self‐attention modules for efficient identification of audio deepfakes. Our module directly compares unprocessed original audio with modified audio by initially applying convolutional operations and dual branches to extract complex characteristics from raw audio signals. These operations are followed by residual connections, which enhance the network's performance. The self‐attention modules are trained in a layered way alongside these fundamental layers to detect multi‐headed attention within audio frames. The StacLoss output represents a customized version of the contrastive loss function. It aids the network in distinguishing between original and modified audios by minimizing the loss between pairs of original audio that have the same identity while maximizing the distance between manipulated audio samples and enhances the process of extracting features compared to standard techniques. The efficacy of the method has been verified by examining a range of audio modifications, and its resilience has been thoroughly assessed on the ASVspoof2019 dataset by comprehensive testing across all possible audio manipulation situations. The proposed Siamese convolutional neural network (CNN) outperformed both machine and deep learning models, achieving impressive metrics. It achieved a remarkable accuracy of 98%, precision of 97%, recall of 96%, F1 score of 96.5%, ROC‐AUC of 99%, and an equal error rate (EER) of 2.95%.https://doi.org/10.1002/eng2.70087audio deepfakedeep learningdeepfakemachine learningSiamese CNN |
| spellingShingle | Ousama A. Shaaban Remzi Yildirim Audio Deepfake Detection Using Deep Learning Engineering Reports audio deepfake deep learning deepfake machine learning Siamese CNN |
| title | Audio Deepfake Detection Using Deep Learning |
| title_full | Audio Deepfake Detection Using Deep Learning |
| title_fullStr | Audio Deepfake Detection Using Deep Learning |
| title_full_unstemmed | Audio Deepfake Detection Using Deep Learning |
| title_short | Audio Deepfake Detection Using Deep Learning |
| title_sort | audio deepfake detection using deep learning |
| topic | audio deepfake deep learning deepfake machine learning Siamese CNN |
| url | https://doi.org/10.1002/eng2.70087 |
| work_keys_str_mv | AT ousamaashaaban audiodeepfakedetectionusingdeeplearning AT remziyildirim audiodeepfakedetectionusingdeeplearning |