ash12321/deepfake-ensemble-detector

Ensemble Deepfake Detector

State-of-the-art ensemble model combining Deep SVDD + Autoencoder for deepfake detection with 77.5% accuracy and 88.75% recall.

Model Description

This ensemble combines two complementary anomaly detection approaches:

1. Deep SVDD - Detects anomalies via hypersphere distance in latent space
2. Autoencoder - Detects anomalies via reconstruction error

Each model has 50% voting weight, and scores are averaged with an optimized threshold.

Performance

Evaluated on 800 test images (CIFAR-10 vs MNIST, CIFAR-10 vs Fashion-MNIST):

Metric	Value
Accuracy	77.50%
Precision	72.22%
Recall	88.75% 🎯
F1 Score	79.46%
Model Agreement	53.00%

Key Strength: 88.75% recall means it catches nearly 9 out of 10 fakes!

Quick Start

from ensemble_model import EnsembleDeepfakeDetector

# Load ensemble (downloads both models automatically)
detector = EnsembleDeepfakeDetector.from_pretrained()

# Predict on image
score, is_fake = detector.predict('image.jpg')
print(f"Deepfake Score: {score:.4f}")
print(f"Is Fake: {is_fake}")

Installation

pip install torch torchvision huggingface-hub pillow

Threshold Options

The ensemble uses an optimized threshold of 0.1163 by default:

# Use different thresholds
detector.set_threshold(0.1163)  # Optimal (default) - 88.75% recall
detector.set_threshold(0.5)     # Conservative - fewer false positives
detector.set_threshold(0.05)    # Sensitive - catch even more fakes

Threshold Comparison:

Threshold	Accuracy	Precision	Recall	Use Case
0.1163 (optimal)	77.5%	72.2%	88.8%	Recommended - Best balance
0.5 (conservative)	66.9%	73.1%	47.5%	Minimize false alarms
0.05 (sensitive)	~70%	~65%	~95%	Maximum detection

Component Models

This ensemble uses:

• ash12321/deep-svdd-anomaly-detection
• ash12321/deepfake-autoencoder-cifar10-v2

How It Works

1. Deep SVDD: Learns a hypersphere around normal images. Fakes fall outside this sphere.
2. Autoencoder: Learns to reconstruct normal images. Fakes have high reconstruction error.
3. Ensemble: Averages both scores (50/50 voting) for robust detection.

Low Model Agreement (53%) indicates the models detect different anomaly types - this is a strength!

Training Data

• CIFAR-10 (natural images)
• CIFAR-100 (natural images)
• STL-10 (natural images)

Limitations

• Trained on natural images - best for detecting distribution shift
• May not generalize to all deepfake types
• Requires RGB images resized to 128x128

Citation

@misc{ensemble-deepfake-detector,
  title={Ensemble Deepfake Detector},
  author={ash12321},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/ash12321/deepfake-ensemble-detector}
}

License

Apache 2.0