Harmonizing Dynamic Frequency Analysis with Attention Mechanisms for Efficient Facial Image Authenticity Detection

Deepfake detection is increasingly critical in security and digital forensics. Our research presents a novel method that synergizes dynamic frequency domain analysis with attention mechanisms to discern Deepfakes more effectively. This approach is developed in response to the inadequacies of existing techniques when confronted with sophisticated image alterations. By leveraging frequency domain subtleties and an advanced attention mechanism, our model achieves heightened accuracy in identifying key facial anomalies. Our experimental results show that DFAM could yield 93.94% accuracy rate which is the best compared with state-of-the-arts on Deepfakes dataset.