AI-vs-Deepfake-vs-Real
| Property | Value |
|---|---|
| Base Architecture | Google ViT (google/vit-base-patch32-224-in21k) |
| Accuracy | 97.50% |
| Model URL | Hugging Face Hub |
What is AI-vs-Deepfake-vs-Real?
AI-vs-Deepfake-vs-Real is a sophisticated image classification model designed to differentiate between artificial, deepfake, and real images. Built on Google's Vision Transformer architecture, it achieves impressive accuracy across all three categories, with particularly strong performance in identifying real images (99.81% F1-score).
Implementation Details
The model leverages the ViT architecture with patch size 32 and resolution 224x224. It demonstrates exceptional classification capabilities with precision scores of 98.97% for artificial images, 94.09% for deepfakes, and 99.70% for real images.
- Built on Google's ViT base model architecture
- Three-way classification system (Artificial, Deepfake, Real)
- High-performance metrics across all categories
- Easy integration with Hugging Face Pipeline or PyTorch
Core Capabilities
- Accurate differentiation between AI-generated, deepfake, and real images
- Research-grade quality assessment for deepfake analysis
- Dataset filtering and content moderation support
- Forensic analysis assistance
- Benchmarking capabilities for deepfake generation models
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to distinguish between three distinct categories (AI-generated, deepfake, and real images) with high accuracy makes it particularly valuable for research and content moderation applications. Its foundation on the ViT architecture provides robust performance across various image types.
Q: What are the recommended use cases?
The model is ideal for research purposes, dataset filtering, forensic analysis, and content moderation. It's particularly useful for evaluating deepfake generation methods and assessing image authenticity in research contexts.
