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# AI-generated_images_detector

This model achieves the following results on the evaluation set:
- Loss: 0.0987
- Accuracy: 0.9736

# To utilize this model

``` python

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}
}


dalle real sd