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---
library_name: llava
license: cc-by-4.0
pipeline_tag: image-text-to-text
---
[[Paper]](https://arxiv.org/pdf/2501.09446) [[github]](https://github.com/zw615/Double_Visual_Defense)
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).
Project page: https://doublevisualdefense.github.io/
## Release
These models are released under the Creative Commons Attribution 4.0 license.
LLNL-DATA-2003001
## Citation
If you find this model useful, please consider citing our paper:
```bibtex
@article{wang2025double,
title={Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness},
author={Wang, Zeyu and Xie, Cihang and Bartoldson, Brian and Kailkhura, Bhavya},
journal={arXiv preprint arXiv:2501.09446},
year={2025}
}
``` |