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

• Published in: AL-Rafidain journal of computer sciences and mathematics Vol. 17; no. 2; pp. 19 - 27
• Main Authors: Al_Dulaimi, Duha Amir, Ibrahim, Laheeb
• Format: Journal Article
• Language: Arabic; English
• Published: Mosul University 23-12-2023
• Subjects: cnn; deep learning; deepfake detection; facenet; pca
• ISSN: 2311-7990; 1815-4816; 2311-7990
• DOI: 10.33899/csmj.2023.181628

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.