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README.md

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 ---
-title: Deepfake Detector App
-emoji: 🌖
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 6.9.0
-app_file: app.py
-pinned: false
+license: mit
+tags:
+- deepfake-detection
+- computer-vision
+- transfer-learning
+- hybrid-cnn
+- pytorch
+datasets:
+- xhlulu/140k-real-and-fake-faces
+metrics:
+- accuracy
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Deepfake Face Detector
+
+A hybrid CNN model for detecting AI-generated (deepfake) face images, built using transfer learning on a dual-stream architecture.
+
+## Model Architecture
+
+This model combines two parallel CNN streams:
+
+- **Spatial Stream** — EfficientNet-B4 pretrained on ImageNet, fine-tuned to detect visible artifacts such as unnatural skin texture, blending edges, and facial inconsistencies
+- **Frequency Stream** — Xception backbone with fixed SRM (Steganalysis Rich Model) filters to detect invisible GAN noise fingerprints in pixel residuals
+- **Fusion Module** — Both stream outputs are concatenated (1792 + 2048 = 3840 features) and passed through a classification head with Dropout regularization
+- **Output** — Sigmoid activation producing a probability score (0 = fake, 1 = real)
+
+## Training Strategy
+
+Three-phase transfer learning approach:
+
+| Phase | Layers Trained | Learning Rate | Epochs |
+|-------|---------------|---------------|--------|
+| 1 | Classifier head only | 1e-3 | 3 |
+| 2 | Top EfficientNet blocks + classifier | 1e-4 | 3 |
+
+## Dataset
+
+Trained and evaluated on [140k Real and Fake Faces](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces)
+
+| Split | Real | Fake | Total |
+|-------|------|------|-------|
+| Train | 50,000 | 50,000 | 100,000 |
+| Valid | 10,000 | 10,000 | 20,000 |
+| Test  | 10,000 | 10,000 | 20,000 |
+
+Fake images are StyleGAN2-generated faces. Real images are sourced from Flickr-Faces-HQ (FFHQ).
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Test Accuracy | 98.59% |
+| Validation Accuracy | 98.55% |
+
+## Usage
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+model = HybridDeepfakeDetector()
+model.load_state_dict(torch.load("deepfake_detector_phase2.pth", map_location="cpu"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+image = Image.open("face.jpg").convert("RGB")
+tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    prob = model(tensor).item()
+
+label = "REAL" if prob > 0.5 else "FAKE"
+print(f"{label} ({prob*100:.1f}% confidence)")
+```
+
+## Limitations
+
+- Optimized for StyleGAN2 generated faces — may perform differently on other GAN architectures
+- Best results on frontal face images
+- Not tested on video deepfakes
+
+## Try It
+
+Live demo available at [Hugging Face Spaces](https://huggingface.co/spaces/AdityaManojShinde/deepfake-detector-app)