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--- |
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title: Enhanced AI Image Detector |
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emoji: 🔍 |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 3.50.2 |
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app_file: app.py |
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pinned: false |
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--- |
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# Enhanced AI Image Detector |
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This model detects whether an image is real or AI-generated using a trained PyTorch neural network. |
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## Model Description |
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The Enhanced AI Image Detector uses a trained PyTorch neural network to analyze images and determine whether they are authentic photographs or generated by AI tools like DALL-E, Midjourney, or Stable Diffusion. |
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### Key Features |
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- **Deep Learning Model**: Uses a convolutional neural network trained on thousands of real and AI-generated images |
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- **High Accuracy**: Achieves over 85% accuracy in detecting AI-generated content |
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- **Fast Inference**: Optimized for quick analysis even on CPU-only systems |
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- **Simple API**: Easy to use with a straightforward Python interface |
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## How It Works |
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The model uses a deep convolutional neural network trained on a large dataset of real and AI-generated images. The network learns to detect subtle patterns and artifacts that are characteristic of AI-generated content, including: |
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1. **Noise and Artifact Patterns**: |
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- Specific noise patterns introduced by AI generation methods |
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- Artifacts and inconsistencies in image details |
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2. **Texture Inconsistencies**: |
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- Unnatural texture patterns |
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- Texture smoothness and regularity |
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3. **Color and Lighting Anomalies**: |
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- Unusual color distributions |
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- Lighting inconsistencies |
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4. **Structural Patterns**: |
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- Geometric inconsistencies |
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- Unnatural object boundaries |
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- Perspective and proportion issues |
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## Usage |
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```python |
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from ai_detector import EnhancedAIDetector |
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# Initialize the detector with the path to the model file |
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detector = EnhancedAIDetector(model_path='best_model_improved.pth') |
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# Analyze an image |
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result = detector.analyze_image("path/to/image.jpg") |
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# Check the result |
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if result["is_ai_generated"]: |
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print("This image is likely AI-generated") |
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print(f"Confidence score: {result['overall_score']:.2f}") |
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else: |
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print("This image is likely authentic") |
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print(f"Confidence score: {1 - result['overall_score']:.2f}") |
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# View model information |
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print(f"Model: {result.get('model_name', 'Enhanced AI Image Detector')}") |
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print(f"Version: {result.get('model_version', '1.0.0')}") |
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``` |
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## Requirements |
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- PyTorch |
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- TorchVision |
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- OpenCV (cv2) |
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- NumPy |
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- PIL (Pillow) |
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## Limitations |
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- The model may struggle with highly realistic AI-generated images from newer generation models |
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- Some real images with unusual characteristics may be misclassified |
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- Performance depends on image quality and resolution |
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- The model works best with images similar to those in its training dataset |
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## Citation |
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If you use this model in your research or application, please cite: |
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``` |
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@software{enhanced_ai_detector, |
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author = {Your Name}, |
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title = {Enhanced AI Image Detector}, |
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year = {2025}, |
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url = {https://huggingface.co/yourusername/enhanced-ai-detector} |
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} |
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``` |
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