Deepfake-Detection-Exp-02-21-ONNX
| Property | Value |
|---|---|
| Base Architecture | ViT-base-patch16-224-in21k |
| Accuracy | 98.84% |
| Model Type | Vision Transformer (ViT) |
| Author | prithivMLmods |
| Hugging Face URL | Model Repository |
What is Deepfake-Detection-Exp-02-21-ONNX?
This is a specialized Vision Transformer model designed for detecting deepfake images with exceptional accuracy. Built on Google's ViT architecture, it achieves an impressive 98.84% accuracy in distinguishing between real and AI-generated images. The model processes images in 224x224 resolution using 16x16 patches, making it particularly effective for social media and web content analysis.
Implementation Details
The model utilizes a binary classification approach, mapping outputs to either 'Deepfake' (0) or 'Real' (1). It demonstrates remarkable precision metrics with 99.62% for deepfake detection and 98.09% for real image identification. The implementation is available in both PyTorch and Hugging Face Pipeline formats, making it accessible for various deployment scenarios.
- Pre-trained on Google's ViT-base-patch16-224-in21k architecture
- Binary classification with high-precision metrics
- Optimized for 224x224 image resolution
- Supports both PyTorch and Hugging Face inference pipelines
Core Capabilities
- High-accuracy deepfake detection (98.84% overall accuracy)
- Balanced performance across both classes (F1-scores: 0.9883 for Deepfake, 0.9885 for Real)
- Efficient processing of standard-resolution images
- Robust performance in content moderation scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional accuracy and balanced performance across both deepfake and real image detection make it stand out. Its implementation of the ViT architecture specifically for deepfake detection provides a strong foundation for practical applications in content moderation and digital forensics.
Q: What are the recommended use cases?
The model is ideal for media authentication, content moderation on social platforms, digital forensics, and educational purposes in AI research. It's particularly suitable for scenarios requiring high-confidence deepfake detection in standard-resolution images.
