Diff-based-AIGD

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Diff-based-AIGD

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# Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images

The prevalence of AI-generated images has evoked concerns regarding the potential misuse of image generation technologies. In response, numerous detection methods aim to identify AI-generated images by analyzing generative artifacts. Unfortunately, most detectors quickly become obsolete with the development of generative models. In this paper, we first design a low-level feature extractor that transforms spatial images into feature space, where different source images exhibit distinct distributions. The pretext task for the feature extractor is to distinguish between images that differ only at the pixel level. This image set comprises the original image as well as versions that have been subjected to varying levels of noise and subsequently denoised using a pre-trained diffusion model. We employ the diffusion model as a denoising tool rather than an image generation tool. Then, we frame the AI-generated image detection task as a one-class classification. We estimate the low-level intrinsic feature distribution of real photographic images and identify features that deviate from this distribution as indicators of AI-generated images. We evaluate our method against over 20 different generative models, including those in GenImage and DRCT-2M datasets. Extensive experiments demonstrate its effectiveness on AI-generated images produced not only by diffusion models but also by GANs, flow-based models, and their variants.

<img src="cover.png" width="1200"/>
 
 The feature visuzaliation of photographic and various AI-generated images.

[**Paper**](https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Beyond_Generation_A_Diffusion-based_Low-level_Feature_Extractor_for_Detecting_AI-generated_CVPR_2025_paper.html)

---

## Demo

We show a simple demo in our repository.

First, we employ a pre-trained low-level feature extractor to transform a spatial suspicious image into an embedding vector. Next, we use a pre-trained Gaussian Mixture Model (GMM) to compute the log-likelihood score of the embedding vector. A suspicious image is classified as AI-generated if its score falls below a threshold determined using a photographic image dataset.

```bash
python demo.py
```

## Pre-trained weights

The weights of the pre-trained low-level feature extractor are available [link](https://pan.baidu.com/s/1oz6W4-aMi_0tK364ksNusg?pwd=35eq).

We provide the low-level features of images from GenImage and DRCT-2M, along with the pre-trained Gaussian Mixture Model (GMM), [link](https://pan.baidu.com/s/1oz6W4-aMi_0tK364ksNusg?pwd=35eq). You can also extract features and train the GMM using our provided code.

## Detailed Procedure

1. Low-level feature extraction & GMM training

```bash
python gmm_train_genimage.py --tag basic_genimage --checkpoint_path ./checkpoints_backbone/basic.pth --train_set_path /localssd/genimage_train --test_set_path /localssd/genimage_val 
```
2. Performance analysis
```bash
python eval_basic_genimage.py
```

## Citation
If you find this work useful, please cite our paper:
```bibtex
@inproceedings{zhong2025beyond,
  title={Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images},
  author={Zhong, Nan and Chen, Haoyu and Xu, Yiran and Qian, Zhenxing and Zhang, Xinpeng},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={8258--8268},
  year={2025}
}

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