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| license: mit |
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| # AnyAttack: Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models |
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| ## 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. |
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| ## 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. |
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| For a detailed explanation of the **AnyAttack** framework and methodology, please visit our **[Project Page](https://jiamingzhang94.github.io/anyattack/)**. |
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| ## 🔗 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/). |
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| ## 📜 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} |
| } |
| ``` |
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| ## ⚠️ Disclaimer |
| This model is intended **for research purposes only**. The misuse of adversarial attacks can have ethical and legal implications. Please use responsibly. |
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| ### ⭐ If you find this model useful, please give it a star on Hugging Face! ⭐ |
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