CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

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: <200ms per image for real-time detection- False Negative Rate: 0.27% (only 2 out of 750 deepfakes missed) Applications:- Content moderation for social media platforms- Forensic analysis and fact-checking- Educational demonstrations of deepfake technology- Research advancement in synthetic media detection This work contributes to the critical challenge of maintaining trust in digital media by providing both generation capabilities (for understanding manipulation techniques) and highly accurate detection systems. The framework is designed with ethical considerations, emphasizing responsible use for research, education, and security applications. Dataset: FaceForensics++ (c40 compression)Framework: PyTorch with timm and Gradio librariesEvaluation Metrics: Accuracy, AUC-ROC, Precision, Recall, F1-Score, Confusion Matrix Author: Huzaifa NasirAffiliation: National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan