Improve dataset card: Add metadata, links, abstract summary, and detection methods

This pull request aims to enhance the `AIGIBench` dataset card by:

- Adding `task_categories: ['image-classification']` to the metadata, aligning with the dataset's purpose of evaluating AI-generated image detectors.
- Including `tags: ['ai-generated-image-detection', 'deepfake-detection', 'generative-models', 'benchmark']` for better discoverability.
- Adding explicit links to the paper ([Hugging Face Papers page](https://huggingface.co/papers/2505.12335)) and the [GitHub repository](https://github.com/HorizonTEL/AIGIBench) at the top of the card.
- Integrating a concise summary from the paper's abstract to provide immediate context.
- Integrating the "Detection Methods" section from the GitHub README to provide more context on how the dataset is used.
- Updating the citation section to use the `@inproceedings` format from the GitHub README, reflecting its acceptance at NeurIPS 2025.

These changes will make the dataset card more informative and easier to navigate for researchers.

README.md

@@ -1,6 +1,14 @@
 ---
 license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+tags:
+- ai-generated-image-detection
+- deepfake-detection
+- generative-models
+- benchmark
 ---
+
 <div align="center">
  <br>
 <h1>Is Artificial Intelligence Generated Image Detection a Solved Problem?</h1>
@@ -16,19 +24,11 @@ license: cc-by-nc-sa-4.0
 
 <sup>1</sup>Nanjing University of Information Science and Technology <sup>2</sup>University of Siena
 
-<!-- <p align="center">
-  <a href='https://github.com/HorizonTEL/AIGIBench'>
-    <img src='https://img.shields.io/badge/Project-Page-pink?style=flat&logo=Google%20chrome&logoColor=pink'>
-  </a>
-  <a href='https://arxiv.org/abs/2505.12335'>
-    <img src='https://img.shields.io/badge/Arxiv-2406.19435-A42C25?style=flat&logo=arXiv&logoColor=A42C25'>
-  </a>
-  <a href='https://arxiv.org/pdf/2505.12335'>
-    <img src='https://img.shields.io/badge/Paper-PDF-yellow?style=flat&logo=arXiv&logoColor=yellow'>
-  </a>
-</p>
--->
-</div> 
+</div>
+
+[Paper](https://huggingface.co/papers/2505.12335) | [GitHub Repository](https://github.com/HorizonTEL/AIGIBench)
+
+AIGIBench is a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art Artificial Intelligence Generated Image (AIGI) detectors. It 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.
 
 **This repository is the official dataset of the AIGIBench.**
 
@@ -105,13 +105,26 @@ AIGIBench comprehensively tests the performance of the detector and builds a tes
 │   │   ├── ...
 ```
 
-## Citation
+## 🔍Detection Methods
+We use the official code for all detection codes and make unified modifications to the input and output. The code we use for training in Setting-II is publicly available above, the corresponding pre-trained checkpoints are publicly available on [Huggingface](https://huggingface.co/HorizonTEL/AIGIBench). Of course, if you need the code from the original paper, the following is the corresponding detection code in the paper：
+- [ResNet-50](https://github.com/huggingface/pytorch-image-models/tree/v0.6.12/timm): Deep Residual Learning for Image Recognition
+- [CNNDetection](https://github.com/PeterWang512/CNNDetection): CNN-generated images are surprisingly easy to spot...for now
+- [GramNet](https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild): Global Texture Enhancement for Fake Face Detection in the Wild
+- [LGrad](https://github.com/chuangchuangtan/LGrad): Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
+- [CLIPDetection](https://github.com/WisconsinAIVision/UniversalFakeDetect): Towards Universal Fake Image Detectors that Generalize Across Generative Models
+- [FreqNet](https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection): FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
+- [NPR](https://github.com/chuangchuangtan/NPR-DeepfakeDetection): Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
+- [DFFreq](https://arxiv.org/abs/2501.15253): Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection
+- [LaDeDa](https://github.com/barcavia/RealTime-DeepfakeDetection-in-the-RealWorld): Real-Time Deepfake Detection in the Real-World
+- [AIDE](https://github.com/shilinyan99/AIDE): A Sanity Check for AI-generated Image Detection
+- [SAFE](https://github.com/Ouxiang-Li/SAFE): Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectives
 
-```
-@article{li2025artificial,
+## 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},
-  journal={arXiv preprint arXiv:2505.12335},
+  booktitle={Advances in Neural Information Processing Systems},
   year={2025}
 }
 ```