Deepfake-Detection-Exp-02-22-ONNX
| Property | Value |
|---|---|
| Base Architecture | ViT (Vision Transformer) |
| Accuracy | 95.16% |
| Input Resolution | 224x224 |
| Model URL | Hugging Face |
What is Deepfake-Detection-Exp-02-22-ONNX?
This is a specialized Vision Transformer-based model designed for detecting deepfake images. Built on Google's vit-base-patch32-224-in21k architecture, it achieves impressive performance with 98.33% precision for deepfake detection and 92.38% for real image identification.
Implementation Details
The model utilizes a Vision Transformer architecture optimized for 224x224 image inputs. It implements a binary classification system, mapping outputs to either 'Deepfake' (0) or 'Real' (1) with high accuracy and reliability.
- Based on Google's ViT architecture
- Binary classification system
- Optimized for standard resolution images
- High precision metrics (98.33% for deepfakes)
Core Capabilities
- Accurate deepfake image detection
- Real-time image classification
- Easy integration via Hugging Face pipeline
- Support for both PyTorch and ONNX formats
Frequently Asked Questions
Q: What makes this model unique?
The model combines high accuracy (95.16%) with practical usability, leveraging the powerful Vision Transformer architecture while maintaining reasonable computational requirements. Its balanced performance in both deepfake detection and real image verification makes it particularly reliable.
Q: What are the recommended use cases?
The model is ideal for content moderation, forensic analysis, security applications, and research purposes. It's particularly suited for platforms requiring automated deepfake detection and educational contexts focused on AI image analysis.
