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-license: mit
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+---
+license: mit
+---
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+# AnyAttack: Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models
+
+## TL;DR
+**AnyAttack** is a powerful adversarial attack model that can transform ordinary images into targeted adversarial examples capable of misleading Vision-Language Models (VLMs). By pre-training on the **LAION-400M dataset**, our model enables a benign image (e.g., a dog) to be misinterpreted by VLMs as any specified content (e.g., "this is violent content"), working across both **open-source** and **commercial** models.
+
+## Model Overview
+**AnyAttack** is designed to generate adversarial examples efficiently and at scale. Unlike traditional adversarial methods, it does not require predefined labels and instead leverages a self-supervised adversarial noise generator trained on large-scale data. 
+
+For a detailed explanation of the **AnyAttack** framework and methodology, please visit our **[Project Page](https://jiamingzhang94.github.io/anyattack/)**.
+
+## 🔗 Links & Resources
+- **Project Page:** [AnyAttack Website](https://jiamingzhang94.github.io/anyattack/)
+- **Paper:** [arXiv](https://arxiv.org/abs/2410.05346/)
+- **Code:** [GitHub](https://github.com/jiamingzhang94/AnyAttack/).
+
+## 📜 Citation
+If you use **AnyAttack** in your research, please cite our work:
+```bibtex
+@inproceedings{zhang2025anyattack,
+    title={Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models},
+    author={Zhang, Jiaming and Ye, Junhong and Ma, Xingjun and Li, Yige and Yang, Yunfan and Yunhao, Chen and Sang, Jitao and Yeung, Dit-Yan},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    year={2025}
+}
+```
+
+## ⚠️ Disclaimer
+This model is intended **for research purposes only**. The misuse of adversarial attacks can have ethical and legal implications. Please use responsibly.
+
+---
+
+### ⭐ If you find this model useful, please give it a star on Hugging Face! ⭐
+
+
+