Layer	Model	Author	License
Base Architecture	google/vit-base-patch16-224-in21k	Google	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

python

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:

bibtex

@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