detectors/CLIP-D/README.md

# ClipBased-SyntheticImageDetection

[![Official Github Repo](https://img.shields.io/badge/Github%20page-222222.svg?style=for-the-badge&logo=github)](https://grip-unina.github.io/ClipBased-SyntheticImageDetection/)
[![Paper](https://img.shields.io/badge/-arXiv-B31B1B.svg?style=for-the-badge)](https://arxiv.org/abs/2312.00195v2)
[![GRIP Research Group Website](https://img.shields.io/badge/-GRIP-0888ef.svg?style=for-the-badge)](https://www.grip.unina.it)

Original Paper:
[Raising the Bar of AI-generated Image Detection with CLIP](https://arxiv.org/abs/2312.00195v2).

Authors: Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner, and Luisa Verdoliva.

## Abstract

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits surprising generalization ability and high robustness across different architectures, including recent commercial tools such as Dalle-3, Midjourney v5, and Firefly. We match the state-of-the-art (SoTA) on in-distribution data and significantly improve upon it in terms of generalization to out-of-distribution data (+6% AUC) and robustness to impaired/laundered data (+13%).

## Please Cite 

```
@inproceedings{cozzolino2023raising,
  author={Davide Cozzolino and Giovanni Poggi and 
  Riccardo Corvi and Matthias Nießner and Luisa 
  Verdoliva},
  title={{Raising the Bar of AI-generated Image 
  Detection with CLIP}}, 
  booktitle={IEEE/CVF Conference on Computer Vision 
  and Pattern Recognition Workshops (CVPRW)},
  year={2024},
}
```