Deepfake-Detection-Exp-02-22
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT) |
| Base Architecture | google/vit-base-patch32-224-in21k |
| Accuracy | 95.16% |
| Hugging Face URL | Model Repository |
What is Deepfake-Detection-Exp-02-22?
Deepfake-Detection-Exp-02-22 is a specialized Vision Transformer (ViT) model designed for distinguishing between deepfake and authentic images. Built on Google's vit-base-patch32-224-in21k architecture, this model demonstrates exceptional performance with 98.33% precision in detecting deepfakes and 92.38% precision for real images.
Implementation Details
The model processes images through a Vision Transformer architecture optimized for 224x224 resolution inputs. It outputs binary classifications: 0 for Deepfake and 1 for Real images. The implementation supports both Hugging Face Pipeline and PyTorch inference methods, making it versatile for different deployment scenarios.
- Binary classification architecture (Deepfake/Real)
- 224x224 image resolution optimization
- Support for both Pipeline and PyTorch deployment
- Pre-trained weights based on ViT architecture
Core Capabilities
- High-precision deepfake detection (98.33%)
- Robust real image verification (92.38%)
- Efficient processing with ViT architecture
- Easy integration with existing pipelines
- Suitable for content moderation systems
Frequently Asked Questions
Q: What makes this model unique?
The model combines state-of-the-art ViT architecture with exceptional accuracy in deepfake detection, achieving over 95% overall accuracy while maintaining high precision for both real and fake image classification.
Q: What are the recommended use cases?
The model is ideal for content moderation, forensic analysis, cybersecurity applications, and research purposes. It's particularly useful in scenarios requiring automated verification of image authenticity.
Q: What are the main limitations?
The model has limitations regarding generalization to novel deepfake techniques, resolution constraints (optimized for 224x224), and potential vulnerability to adversarial attacks. It may also show bias based on training data limitations.
