Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training

• URL: http://arxiv.org/abs/2502.11455v1
• Date: Mon, 17 Feb 2025 05:28:47 GMT
• Title: Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training
• Authors: Fenghua Weng, Jian Lou, Jun Feng, Minlie Huang, Wenjie Wang,
• Abstract summary: We propose $textitAdversary-aware DPO (ADPO)$, a novel training framework that explicitly considers adversarial.<n>$textitADPO$ integrates adversarial training into DPO to enhance the safety alignment of VLMs under worst-case adversarial perturbations.<n>$textitADPO$ ensures that VLMs remain robust and reliable even in the presence of sophisticated jailbreak attacks.
• Score: 50.829723203044395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety alignment is critical in pre-training large language models (LLMs) to generate responses aligned with human values and refuse harmful queries. Unlike LLM, the current safety alignment of VLMs is often achieved with post-hoc safety fine-tuning. However, these methods are less effective to white-box attacks. To address this, we propose $\textit{Adversary-aware DPO (ADPO)}$, a novel training framework that explicitly considers adversarial. $\textit{Adversary-aware DPO (ADPO)}$ integrates adversarial training into DPO to enhance the safety alignment of VLMs under worst-case adversarial perturbations. $\textit{ADPO}$ introduces two key components: (1) an adversarial-trained reference model that generates human-preferred responses under worst-case perturbations, and (2) an adversarial-aware DPO loss that generates winner-loser pairs accounting for adversarial distortions. By combining these innovations, $\textit{ADPO}$ ensures that VLMs remain robust and reliable even in the presence of sophisticated jailbreak attacks. Extensive experiments demonstrate that $\textit{ADPO}$ outperforms baselines in the safety alignment and general utility of VLMs.

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