Face Forgery Detection

Face forgery detection aims to distinguish authentic facial images and videos from manipulated ones ("deepfakes"), primarily using deep learning models to identify subtle inconsistencies. Current research emphasizes improving the generalization of these models across diverse forgery techniques and datasets, focusing on architectures like Vision Transformers (ViTs) and incorporating multi-modal information (e.g., image, noise, text) for more robust detection. This field is crucial for combating misinformation, protecting individual privacy, and ensuring the security of identity verification systems, driving significant advancements in both computer vision and AI explainability.