AI-generated_images_detector
This model achieves the following results on the evaluation set:
- Loss: 0.0987
- Accuracy: 0.9736
To utilize this model
from PIL import Image
from transformers import pipeline
classifier = pipeline("image-classification", model="NYUAD-ComNets/NYUAD_AI-generated_images_detector")
image=Image.open("path_to_image")
pred=classifier(image)
print(pred)
Training and evaluation data
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0431 | 0.55 | 100 | 0.1672 | 0.9568 |
| 0.0139 | 1.1 | 200 | 0.2338 | 0.9398 |
| 0.0201 | 1.66 | 300 | 0.1291 | 0.9655 |
| 0.0023 | 2.21 | 400 | 0.1147 | 0.9709 |
| 0.0033 | 2.76 | 500 | 0.0987 | 0.9736 |
BibTeX entry and citation info
@article{aldahoul2024detecting,
title={Detecting AI-Generated Images Using Vision Transformers: A Robust Approach for Safeguarding Visual Media Integrity},
author={AlDahoul, Nouar and Zaki, Yasir},
journal={Available at SSRN},
year={2024}
}
@misc{ComNets,
url={https://huggingface.co/NYUAD-ComNets/NYUAD_AI-generated_images_detector](https://huggingface.co/NYUAD-ComNets/NYUAD_AI-generated_images_detector)},
title={NYUAD_AI-generated_images_detector},
author={Nouar AlDahoul, Yasir Zaki}
}