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Cover image for Deepfake Detection System Problem: Deepfake accessibility
Deepfake Detection System Problem: Deepfake accessibility creates real risks—misinformation, identity fraud, digital trust erosion. I built a production-grade detector: fast, accurate, explainable, CPU-deployable. Approach: Transfer learning with ResNet-50 (25.5M params, pre-trained ImageNet-1K V2). Rather than training from scratch on limited data, I leveraged rich hierarchical features that transfer exceptionally well to detecting artifact patterns (blending boundaries, color mismatches, compression artifacts). Key Decisions: Face-Centric Preprocessing: OpenCV DNN crops faces to 224×224, concentrating attention on artifact-rich regions (eyes, mouth, jawlines). Custom Classification Head: Bottleneck design (2048→512→1) with Focal Loss, label smoothing, progressive unfreezing—prevents overfitting on ~4,000 Ciplab images. Explainable AI: Custom Grad-CAM generates attention heatmaps showing exactly where the model detected artifacts. Real-World Robustness: Trained on JPEG compression, Gaussian noise, blur augmentations to handle compressed video feeds. Results: AUC-ROC: 0.9424 | Accuracy: 87.25% | Precision/Recall: 87.27%/84.81% Inference: <100ms per face on CPU Live on Hugging Face Spaces (no GPU) Tech: PyTorch, OpenCV, Albumentations, Gradio. Images, videos, live webcam—one codebase. Demonstrates: End-to-end AI engineering with deliberate trade-offs (efficiency over raw accuracy), training discipline (Focal Loss, Cosine Annealing), and production-first thinking (CPU compatibility, interpretability, real-world robustness). Demo: https://huggingface.co/spaces/Shri04/deepfake-detector
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