AI-vs-Deepfake-vs-Real-Siglip2
| Property | Value |
|---|---|
| Model Type | Image Classification |
| Base Model | google/siglip2-base-patch16-224 |
| Architecture | SiglipForImageClassification |
| Accuracy | 99.05% |
| HuggingFace URL | Model Repository |
What is AI-vs-Deepfake-vs-Real-Siglip2?
AI-vs-Deepfake-vs-Real-Siglip2 is a sophisticated image classification model designed to differentiate between three types of images: AI-generated content, deepfake manipulations, and authentic photographs. Built on the google/siglip2-base-patch16-224 architecture, this model achieves remarkable accuracy in distinguishing synthetic from real content.
Implementation Details
The model employs the SiglipForImageClassification architecture and processes images through a vision-language encoder. It categorizes inputs into three distinct classes with exceptional precision: AI-generated (97.94%), Deepfake (99.31%), and Real (99.92%). The implementation includes a straightforward pipeline using the Transformers library and can be easily integrated using PyTorch.
- Fine-tuned from google/siglip2-base-patch16-224
- Employs vision-language encoding for robust classification
- Supports RGB image input with automatic preprocessing
- Provides probability scores for each classification category
Core Capabilities
- Accurate identification of AI-generated images (98.74% F1-score)
- Precise detection of deepfake manipulations (98.56% F1-score)
- Reliable authentication of real photographs (99.85% F1-score)
- Real-time classification with probability scoring
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional accuracy across all three categories (AI, Deepfake, and Real) sets it apart, with an overall accuracy of 99.05%. Its ability to distinguish between AI-generated and deepfake content is particularly valuable in today's digital landscape.
Q: What are the recommended use cases?
The model is ideal for content verification, social media filtering, digital forensics, and news authentication. It can be integrated into platforms that need to flag synthetic or manipulated content, supporting fact-checking initiatives and content moderation systems.
