Al-Rafidain Journal of Computer Sciences and Mathematics (Dec 2023)
Deepfake Detection Model Based on Combined Features Extracted from Facenet and PCA Techniques
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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