An integrated deep learning deepfakes detection method (IDL-DDM)
Deepfakes have fascinated enormous attention in recent times ascribable to the consequences of threats in video manipulation. Consequently, such manipulation via intelligent algorithm contributes to more crucial circumstances as electronic media integrity become a challenging concern. Furthermore, s...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28774/1/An%20Integrated%20Deep%20Learning%20Deepfakes%20Detection%20Method%20%28IDL-DDM%29.pdf http://eprints.utem.edu.my/id/eprint/28774/ https://link.springer.com/chapter/10.1007/978-981-99-6690-5_6 |
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| Summary: | Deepfakes have fascinated enormous attention in recent times ascribable to the consequences of threats in video manipulation. Consequently, such manipulation via intelligent algorithm contributes to more crucial circumstances as electronic media integrity become a challenging concern. Furthermore, such unauthentic content is being composed and outstretched across social media platforms as detecting deepfakes videos is becoming harder nowadays. Nevertheless, various detection methods for deepfakes have been schemed, and the accuracy of such detection models still emerges as an open issue, particularly for research communities. We proposed an integrated deep learning deepfakes detection model namely IDL-DDM to overcome ongoing criticism, i.e., difficulties in identifying the fake videos more accurately. The proposed IDL-DDM comprises side-by-side deep learning algorithms such as Multilayer Perceptron and Convolutional Neural Network (CNN). In addition, the Long Short-Term Memory (LSTM) approach is applied consecutively after CNN in order to grant sequential processing of data and overcome learning dependencies. Using this learning algorithm, several facial region characteristics such as eyes, nose, and mouth are extracted and further transformed into numerical form with the intention to identify video frames more precisely. The experiments were performed via different datasets such as the Deepfakes Detection Challenge Dataset (DFDC) and Unseen (YouTube Live) videos which comprise a wealth of original and fake videos. The experimental results represent a higher achievement for the IDL-DDM in contrast to other previous similar works. |
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