FaceForge: A Deep Learning Framework for Facial Manipulation Generation and Detection

FaceForge: A Deep Learning Framework for Facial Manipulation Generation and Detection This paper presents FaceForge, a comprehensive deep learning framework for generating and detecting facial manipulations (deepfakes). We develop a Vision Transformer-based generator architecture with 252 million parameters that learns to synthesize realistic face swaps, and an XceptionNet-based detector that achieves 99.33% accuracy with an AUC-ROC of 0.9995 in distinguishing authentic faces from deepfakes. Key Contributions:- Novel Vision Transformer-based generator with dual ViT encoders, cross-attention mechanisms, transformer decoders, and CNN upsamplers for high-quality face synthesis- State-of-the-art XceptionNet-based…