Update README.md

README.md

@@ -1,3 +1,101 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- ai-generated images
+- ai-generated image detection
+- test-set
+- deepfake
+- forgery-detection
+- computer-vision
+task_categories:
+- image-classification
+language:
+- en
+dataset_info:
+  features:
+    - name: file_name
+      dtype: string
+      description: "Relative path to the image under root."
+    - name: image
+      dtype: image
+    - name: is_real
+      dtype: string
+    - name: content_type
+      dtype: string
+  data_files:
+    - split: test
+      path: test.parquet
+---
+
+# 🌊 Mirage-Test Dataset
+
+[![Paper](https://img.shields.io/badge/arXiv-2511.08423-B31B1B.svg)](https://arxiv.org/abs/2511.08423)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github)](https://github.com/yunncheng/OmniAID)
+[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/Yunncheng/OmniAID/tree/main)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Yunncheng/OmniAID-Demo)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+
+**Mirage-Test** is a modern **test-only dataset** for benchmarking AI-generated image detection models. 
+It contains **real** (`0_real`) and **fake** (`1_fake`) images across five distinct content domains, designed to evaluate generalization across diverse visual semantics. 
+The fake images are generated using state-of-the-art generative models specifically optimized for perceptual realism and visual fidelity.
+
+> 📌 **This dataset is for evaluation only. No training split is provided.**
+
+## 📁 Dataset Structure
+
+Images are organized hierarchically by content type and authenticity:
+
+```bash
+Mirage-Test/
+├── Animal/
+│ ├── 0_real/ # Real animal photos
+│ └── 1_fake/ # AI-generated animal images
+├── Anime/
+│ └── 1_fake/ # AI-generated anime-style images
+├── Human/
+│ ├── 0_real/ # Real human photos
+│ └── 1_fake/ # AI-generated human images
+├── Object/
+│ ├── 0_real/ # Real object photos
+│ └── 1_fake/ # AI-generated object images
+├── Scene/
+│ ├── 0_real/ # Real landscape/architecture photos
+│ └── 1_fake/ # AI-generated scenes images
+├── metadata.parquet
+└── README.md
+```
+
+- **Total samples**: 49000
+
+## 📥 Downloading Raw Files
+
+To download the dataset with original folder structure:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="Yunncheng/Mirage-Test",
+    repo_type="dataset",
+    local_dir="./Mirage-Test"
+)
+```
+
+## 🙏 Acknowledgements
+
+- Generated using state-of-the-art diffusion models (e.g., [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux))
+- Real images sourced from publicly available, royalty-free image platforms (e.g., [Pexels](https://www.pexels.com/))
+
+
+## 📝 Citation
+
+If you find this work useful for your research, please cite our paper:
+
+```bibtex
+@article{guo2025omniaid,
+  title={OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild},
+  author={Guo, Yuncheng and Ye, Junyan and Zhang, Chenjue and Kang, Hengrui and Fu, Haohuan and He, Conghui and Li, Weijia},
+  journal={arXiv preprint arXiv:2511.08423},
+  year={2025}
+}
+```