Improve model card: Add pipeline tag, paper/GitHub links, abstract, and citation

This PR enhances the model card by:

- Adding the `pipeline_tag: image-classification` to the metadata, which will improve the model's discoverability on the Hub.
- Including a direct link to the paper on Hugging Face Papers.
- Adding an explicit link to the GitHub repository for easy access to the associated code and dataset.
- Incorporating the paper's abstract to provide a more comprehensive overview of the model.
- Adding the BibTeX citation for proper academic attribution.
- Restructuring the content for better readability, placing key information and links at the forefront.

README.md

@@ -1,8 +1,17 @@
 ---
-license: cc-by-nc-sa-4.0
 datasets:
 - HorizonTEL/AIGIBench
+license: cc-by-nc-sa-4.0
+pipeline_tag: image-classification
 ---
+
+This repository hosts the official pre-trained checkpoints for the **AIGIBench**, a comprehensive benchmark for evaluating Artificial Intelligence Generated Image (AIGI) detectors, as presented in the paper [Is Artificial Intelligence Generated Image Detection a Solved Problem?](https://huggingface.co/papers/2505.12335).
+
+The associated code and dataset can be found on GitHub: [https://github.com/HorizonTEL/AIGIBench](https://github.com/HorizonTEL/AIGIBench)
+
+## Abstract
+The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI this http URL and code are publicly available at: this https URL .
+
 <div align="center">
  <br>
 <h1>Is Artificial Intelligence Generated Image Detection a Solved Problem?</h1>
@@ -24,5 +33,15 @@ datasets:
 
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+## Citation
+```bibtex
+@inproceedings{li2025artificial,
+  title={Is Artificial Intelligence Generated Image Detection a Solved Problem?},
+  author={Li, Ziqiang and Yan, Jiazhen and He, Ziwen and Zeng, Kai and Jiang, Weiwei and Xiong, Lizhi and Fu, Zhangjie},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2025}
+}
+```
+
 ## Contact
 If you have any question about this project, please feel free to contact 247918horizon@gmail.com