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 detector achieving 99.33% accuracy and 0.9995 AUC-ROC on FaceForensics++ dataset- Comprehensive evaluation with only 10 misclassifications out of 1,500 test samples (0.67% error rate)- Interactive web-based demonstration system using Gradio for real-time deepfake detection- Efficient training methodology completing in 19 hours on CPU infrastructure The system is trained on the FaceForensics++ dataset comprising 10,000 face images. Our detector significantly outperforms existing methods including MesoNet (83.1%), base XceptionNet (95.3%), EfficientNet-B4 (96.8%), Capsule Networks (97.4%), and Face X-ray (98.1%). Technical Highlights:- Generator: 252M parameters with L1 reconstruction loss, achieving 0.204 validation loss- Detector: 22M parameters with cross-entropy loss and data augmentation- Training: AdamW optimizer with cosine annealing learning rate schedule- Inference: