Dynamic and Static Features Extraction for Deepfake Detection

Deepfake has emerged as a significant concern due to its ability to generate fake images and synthesize realistic videos. The increasing development of new techniques for deep-fake creation raises concerns about the cross-forgery issue. Cross-forgery indicates that a model is initially trained to recognize a particular fake and must work against a different unknown forgery. Training a model needs substantial quantities of data, a challenge compounded if the deepfake generation technique is relatively new. Addressing cross-forgery is a critical and essential challenge that requires resolution. In order to solve cross-forgery, our effort presents a method that combines dynamic and static features to identify forgery. For the static component, we extract features similar to general deepfake detection techniques using a single RGB frame as input. Simultaneously, we utilize optical flow analysis to capture changes between consecutive frames for the dynamic part. Our experiments reveal a clear advantage in utilizing combined features, which is particularly evident in cross-forgery scenarios. Specifically, when encountering certain categories, the performance improvement is significant, demonstrating four times better than single-feature models.