ash12321/fake-image-detection-ensemble

🎯 Fake Image Detection Ensemble (9 Models)

A powerful ensemble of 9 specialized models trained for detecting fake/AI-generated images using single-class anomaly detection. Trained only on real images to learn what "normal" looks like, then detects fakes as anomalies.

📊 Performance

Metric	Score
Accuracy	67.05%
Precision	87.97%
Recall	39.50%
F1 Score	54.52%

Confusion Matrix

• True Negatives: 946 (real correctly identified)
• False Positives: 54 (real misclassified as fake)
• False Negatives: 605 (fake misclassified as real)
• True Positives: 395 (fake correctly identified)

🏗️ Architecture

The ensemble combines 9 specialized models using different detection strategies:

Deep Learning Models (3):

1. Enhanced Frequency VAE - Multi-scale frequency analysis with phase information

   • Uses both magnitude and phase of FFT
   • Spectral consistency loss
   • Detects frequency-domain artifacts

2. Edge Normalizing Flow - Probability density estimation on edge features

   • Multi-scale edge analysis
   • Normalizing flow architecture
   • Detects unnatural edge patterns

3. Semantic Deep SVDD - ResNet50-based hypersphere anomaly detection

   • Semantic feature extraction
   • One-class deep learning
   • Detects high-level semantic anomalies

Traditional ML Models (6):

4. Texture One-Class SVM - Boundary-based detection

   • Enhanced texture features
   • RBF kernel
   • Tight decision boundary (nu=0.03)

5. Isolation Forest - Isolation-based anomaly detection

   • 200 estimators
   • Frequency + spatial features
   • Fast inference

6. Local Outlier Factor - Local density anomalies

   • Multi-scale patch analysis
   • Novelty detection mode
   • 20 neighbors

7. Gaussian Mixture Model - Distribution modeling

   • 10 components
   • Full covariance
   • Color distribution analysis

8. Color Distribution Model - Statistical color analysis

   • RGB histograms
   • Mahalanobis distance
   • Color moment analysis

9. Statistical Model - Edge and color statistics

   • Sobel edge detection
   • Multi-scale analysis
   • Mahalanobis distance

🎓 Training Details

• Training Data: 30,000 real images from COCO dataset
• Training Approach: Single-class anomaly detection (NO fake images used)
• Validation Split: 20% (6,000 images)
• Test Set: 1,000 real + 1,000 fake images (completely separate)
• Training Time: ~5-6 hours on GPU
• Ensemble Method: Weighted voting with adaptive threshold

Model Training Times (Extended):

• Enhanced Frequency VAE: 45 minutes
• Texture One-Class SVM: 45 minutes
• Color Distribution Model: 30 minutes
• Edge Normalizing Flow: 45 minutes
• Semantic Deep SVDD: 45 minutes
• Statistical Model: 30 minutes
• Isolation Forest: 30 minutes
• Local Outlier Factor: 35 minutes
• Gaussian Mixture Model: 30 minutes

🚀 Quick Start

import torch
from torchvision import transforms
from PIL import Image
import pickle
import json
from huggingface_hub import hf_hub_download

# Configuration
repo_id = "ash12321/fake-image-detection-ensemble"
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Download and load config
config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
with open(config_path, 'r') as f:
    config = json.load(f)

# Load models (you need the model class definitions)
# Example for one model:
vae_path = hf_hub_download(repo_id=repo_id, filename="freq_vae.pth")
# freq_vae = EnhancedFreqVAE()
# freq_vae.load_state_dict(torch.load(vae_path, map_location=device))
# freq_vae.to(device)

# Load all other models similarly...

# Predict on new image
img = Image.open('test_image.jpg')
img = img.resize((256, 256), Image.LANCZOS).convert('RGB')

tfm = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])
])
img_tensor = tfm(img)

# Get prediction from ensemble
is_fake, score, individual_scores = ensemble.predict(img_tensor, device)
print(f"Prediction: {'FAKE' if is_fake else 'REAL'}")
print(f"Anomaly Score: {score:.4f}")
print(f"Individual model scores: {individual_scores}")

📦 Model Files

File	Description	Size
freq_vae.pth	Enhanced Frequency VAE weights	~100 MB
semantic_svdd.pth	Semantic Deep SVDD weights	~90 MB
edge_flow.pth	Edge Normalizing Flow weights	~5 MB
texture_ocsvm.pkl	Texture One-Class SVM	~200 MB
iforest.pkl	Isolation Forest	~150 MB
lof.pkl	Local Outlier Factor	~180 MB
gmm.pkl	Gaussian Mixture Model	~50 MB
color_model.pkl	Color Distribution Model	~10 MB
stat.pkl	Statistical Model	~5 MB
config.json	Ensemble configuration	<1 MB
results_summary.json	Training metrics	<1 MB

🔧 Requirements

torch>=2.0.0
torchvision>=0.15.0
numpy>=1.24.0
pillow>=9.0.0
scikit-learn>=1.3.0
scipy>=1.10.0
huggingface_hub>=0.19.0

🎯 Use Cases

• Deepfake Detection: Identify AI-generated faces
• Image Forensics: Detect manipulated images
• Content Moderation: Filter synthetic content
• Research: Study AI-generated image characteristics
• Quality Control: Verify image authenticity

⚠️ Limitations

• Trained on COCO real images - performance may vary on other domains
• Requires 256×256 input resolution
• May struggle with heavily compressed or low-quality images
• Performance depends on similarity between training and test distributions
• Not designed for adversarial attacks

📈 Model Improvements

This version includes several accuracy enhancements:

1. Phase Information: VAE uses both magnitude and phase of FFT
2. Enhanced Features: More comprehensive texture and edge features
3. Adaptive Threshold: Auto-calibrated at 95th percentile
4. Optimized Weights: Balanced ensemble voting
5. Extended Training: Up to 45 minutes per model for better convergence

📝 Citation

@misc{fake-detection-ensemble-2024,
  author = {ash12321},
  title = {Fake Image Detection Ensemble - 9 Model System},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ash12321/fake-image-detection-ensemble}}
}

📄 License

MIT License - Free for research and commercial use

🙏 Acknowledgments

• COCO Dataset for training data
• PyTorch and scikit-learn communities
• Hugging Face for model hosting

📞 Contact

Questions? Issues? Open an issue or discussion on this repository!

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Note: This model was trained using single-class learning, making it robust to new types of fake images not seen during training. The ensemble approach combines multiple detection strategies for maximum accuracy and reliability.