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--- |
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library_name: llava |
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license: cc-by-4.0 |
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pipeline_tag: image-text-to-text |
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--- |
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[[Paper]](https://arxiv.org/pdf/2501.09446) [[github]](https://github.com/zw615/Double_Visual_Defense) |
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A Delta2-LLaVA-v1.5-7B Model that is adversarially visual instruction tuned with LLaVA1.5 data to reach non-robust-VLM helpfulness levels on clean data while being robust on adversarially attacked data. This model is presented in the paper [Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness](https://huggingface.co/papers/2501.09446). |
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Project page: https://doublevisualdefense.github.io/ |
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## Release |
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These models are released under the Creative Commons Attribution 4.0 license. |
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LLNL-DATA-2003001 |
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## Citation |
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If you find this model useful, please consider citing our paper: |
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```bibtex |
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@article{wang2025double, |
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title={Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness}, |
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author={Wang, Zeyu and Xie, Cihang and Bartoldson, Brian and Kailkhura, Bhavya}, |
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journal={arXiv preprint arXiv:2501.09446}, |
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year={2025} |
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} |
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``` |
