Deepfake Detection Model Based on Combined Features Extracted from Facenet and PCA Techniques

Section: Research Paper
Published
Jun 25, 2025
Pages
19-27

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.

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How to Cite

Mohammad Ibrahim, L., ضحى, Amir Al_Dulaimi, D., & لهیب. (2025). Deepfake Detection Model Based on Combined Features Extracted from Facenet and PCA Techniques. AL-Rafidain Journal of Computer Sciences and Mathematics, 17(2), 19–27. Retrieved from https://rjps.uomosul.edu.iq/index.php/csmj/article/view/19742