Abstract
This is the official code for the paper "Double Visual Defense: Adversarial Pre-training and Instruction
Tuning for Improving Vision-Language Model Robustness". This code can be used to produce vision
language models (VLMs), like LLaVA, with enhanced robustness to adversarial attacks (e.g. jailbreaks).
- Developers:
-
Bartoldson, Brian [1] ; Wang, Zeyu [1] ; Kailkhura, Bhavya [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2024-12-12
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 154189
- Site Accession Number:
- LLNL-CODE-2002980
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Bartoldson, Brian, Wang, Zeyu, and Kailkhura, Bhavya.
Double Visual Defense.
Computer Software.
https://github.com/zw615/Double_Visual_Defense.
USDOE National Nuclear Security Administration (NNSA).
12 Dec. 2024.
Web.
doi:10.11578/dc.20250416.3.
Bartoldson, Brian, Wang, Zeyu, & Kailkhura, Bhavya.
(2024, December 12).
Double Visual Defense.
[Computer software].
https://github.com/zw615/Double_Visual_Defense.
https://doi.org/10.11578/dc.20250416.3.
Bartoldson, Brian, Wang, Zeyu, and Kailkhura, Bhavya.
"Double Visual Defense." Computer software.
December 12, 2024.
https://github.com/zw615/Double_Visual_Defense.
https://doi.org/10.11578/dc.20250416.3.
@misc{
doecode_154189,
title = {Double Visual Defense},
author = {Bartoldson, Brian and Wang, Zeyu and Kailkhura, Bhavya},
abstractNote = {This is the official code for the paper "Double Visual Defense: Adversarial Pre-training and Instruction
Tuning for Improving Vision-Language Model Robustness". This code can be used to produce vision
language models (VLMs), like LLaVA, with enhanced robustness to adversarial attacks (e.g. jailbreaks).},
doi = {10.11578/dc.20250416.3},
url = {https://doi.org/10.11578/dc.20250416.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250416.3}},
year = {2024},
month = {dec}
}