CapCheck AI Image Detection
Vision Transformer (ViT) fine-tuned for detecting AI-generated images.
Model Lineage & Attribution
This model builds on the work of others:
| Layer | Model | Author | License |
|---|---|---|---|
| Base Architecture | google/vit-base-patch16-224-in21k | Apache 2.0 | |
| AI Detection Fine-tune | dima806/ai_vs_real_image_detection | dima806 | Apache 2.0 |
| This Model | capcheck/ai-image-detection | CapCheck | Apache 2.0 |
Special thanks to:
- Google for the Vision Transformer (ViT) architecture
- dima806 for fine-tuning on the CIFAKE dataset for AI image detection
Model Description
- Architecture: ViT-Base (86M parameters)
- Input Size: 224x224 pixels
- Training Data: CIFAKE dataset (AI-generated vs real images)
- Task: Binary classification (Real vs Fake/AI-generated)
Usage
from transformers import pipeline
detector = pipeline("image-classification", model="capcheck/ai-image-detection")
result = detector("path/to/image.jpg")
# Output:
# [{"label": "Fake", "score": 0.95}, {"label": "Real", "score": 0.05}]
Labels
| Label | Description |
|---|---|
Real | Authentic photograph or real-world image |
Fake | AI-generated or synthetically created image |
Performance
This model was trained on the CIFAKE dataset. Performance on modern AI generators (Flux, Midjourney v6, DALL-E 3, Stable Diffusion 3) may vary.
See dima806's model card for detailed training metrics.
Limitations
- Trained primarily on older AI generators (pre-2024)
- May have reduced accuracy on:
- Very new AI generators not in training data
- Heavily compressed images (low JPEG quality)
- Images smaller than 224x224 pixels
- Works best on images with clear subjects
Intended Use
- Content moderation and authenticity verification
- Research into AI-generated content detection
- Educational purposes
Not intended for:
- Making consequential decisions without human review
- Law enforcement evidence without corroborating sources
Ethical Considerations
- This tool is not 100% accurate - false positives harm legitimate creators
- False negatives can allow misinformation to spread
- Use in conjunction with other verification methods
- Human review is recommended for high-stakes decisions
Roadmap
Current Version (v1.0.0)
Base model from dima806's CIFAKE-trained ViT. Solid foundation for AI detection.
Planned Improvements
Phase 1: Modern Generator Training
- Fine-tune on images from Flux, Midjourney v6, DALL-E 3, Stable Diffusion 3
- Target: Reduce false negatives on 2024+ AI generators
Phase 2: False Positive Reduction
- Curate dataset of real images commonly flagged as AI
- Photography edge cases: HDR, heavy editing, digital art
- Target: <5% false positive rate
Phase 3: Continuous Updates
- Quarterly re-training as new generators emerge
- Community feedback integration
- Benchmark against latest AI generators
Contributing
We welcome:
- Dataset contributions (properly licensed images)
- Bug reports and false positive/negative examples
- Benchmark results on new generators
Join the discussion: https://huggingface.co/capcheck/ai-image-detection/discussions
License
Apache 2.0 (inherited from Google ViT and dima806's fine-tuned model)
Citation
If you use this model, please cite:
@misc{capcheck-ai-detection,
author = {CapCheck},
title = {AI Image Detection Model},
year = {2024},
publisher = {HuggingFace},
url = {https://huggingface.co/capcheck/ai-image-detection},
note = {Based on dima806/ai_vs_real_image_detection, fine-tuned from google/vit-base-patch16-224-in21k}
}
Changelog
v1.0.0 (Initial Release)
- Published base model from dima806/ai_vs_real_image_detection
- Added proper attribution and documentation
- Established as CapCheck's source of truth for AI image detection