Deepfake Detection Model Based on Combined Features Extracted from Facenet and PCA Techniques | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Volume 17, Issue 2, December 2023, Pages 19-27 PDF (467.89 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2023.181628 | ||
| Authors | ||
| Duha Amir Al_Dulaimi* 1; Laheeb Mohammad Ibrahim2 | ||
| 1Department of Biology/College of Education for Girls/University of Mosul/Mosul/Iraq | ||
| 2Software Dept. / college of computer science and Math. / Mosul University / mosul / iraq | ||
| Abstract | ||
| Recently, the increase in the emergence of fake videos that have a high degree of accuracy makes it difficult to distinguish from real ones. This is due to the rapid development of deep-learning techniques, especially Generative Adversarial Networks (GAN). The harmful nature of deepfakes urges immediate action to improve the detection of such videos. In this work, we proposed a new model to detect deepfakes based on a hybrid approach for feature extraction by using 128-identity features obtained from facenet_CNN combined with most powerful 10-PCA features. All these features are extracted from cropped faces of 10 frames for each video. FaceForensics++ (FF++) dataset was used to train and test the model, which gave a maximum test accuracy of 0.83, precision of 0.824 and recall value of 0.849. | ||
| Keywords | ||
| CNN; Deepfake Detection; Deep learning; Facenet; PCA | ||
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