Enhancing Deepfake Detection With Diversified Self-Blending Images and Residuals

Abstract

The advancement of deep forgery technology has significantly impacted the credibility of media content, making the detection of deep forgeries crucial for ensuring media security. Although research on deepfake detection methods has been progressively advancing, current approaches predominantly rely on detecting and identifying artifacts. As deep forgery technology continually improves, high-quality synthetic images and those produced through reconstruction methods have become increasingly sophisticated, rendering artifact and trace detection methods somewhat limited. To address this issue, we introduce a deep forgery detection method that integrates deep neural networks with fine-grained artifact features. Our proposed method simulates diverse facial synthesis data by employing facial color conversion, facial frequency domain conversion, and facial mask deformation and blurring. This trains the deepfake detection model to adapt to various synthesis techniques. The classifier model is trained using multiple perturbations of authentic images, with fine-grained artifact features ensuring the stability of the detection process. Our approach achieves superior accuracy and AUC values on the FF++ and WildDeepfake datasets, demonstrating its effectiveness and adaptability in detecting deep forgeries.