标题： 跨越视觉语言模型在跨模态识别不安全概念中的差距 显示英文标题

标题： Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities

摘要： 视觉-语言模型（VLMs）由于其内部的道德标准和强大的推理能力，被越来越多地应用于识别不安全或不适当图像。 然而，尚不清楚它们在不同模态（如文本和图像）中是否能够识别各种不安全概念。 为了解决这个问题，我们首先整理了UnsafeConcepts数据集，包含75个不安全概念，即“万字符”、“性骚扰”和“袭击”，以及相关的1500张图像。 然后，我们对VLMs的感知（概念识别）和对齐（道德推理）能力进行了系统评估。 我们评估了八种流行的VLMs，并发现尽管大多数VLMs能准确感知不安全概念，但有时会将这些概念错误地分类为安全。 我们还发现开源VLMs在区分视觉和文本不安全概念方面存在一致的模态差距。 为了弥合这一差距，我们引入了一种简化的基于强化学习（RL）的方法，使用近端策略优化（PPO）来增强从图像中识别不安全概念的能力。 我们的方法直接基于VLM响应的奖励分数，无需收集人工标注的偏好数据来训练新的奖励模型。 实验结果表明，我们的方法在增强图像上的VLM对齐效果方面有效，同时保持了通用能力。 它优于监督微调（SFT）和直接偏好优化（DPO）等基线方法。 我们希望我们的数据集、评估结果和提出的对齐解决方案能为社区在推进安全VLMs方面的努力做出贡献。 显示英文摘要

摘要： Vision-language models (VLMs) are increasingly applied to identify unsafe or inappropriate images due to their internal ethical standards and powerful reasoning abilities. However, it is still unclear whether they can recognize various unsafe concepts when presented in different modalities, such as text and images. To address this, we first compile the UnsafeConcepts dataset, featuring 75 unsafe concepts, i.e., ``Swastika,'' ``Sexual Harassment,'' and ``Assaults,'' along with associated 1.5K images. We then conduct a systematic evaluation of VLMs' perception (concept recognition) and alignment (ethical reasoning) capabilities. We assess eight popular VLMs and find that, although most VLMs accurately perceive unsafe concepts, they sometimes mistakenly classify these concepts as safe. We also identify a consistent modality gap among open-source VLMs in distinguishing between visual and textual unsafe concepts. To bridge this gap, we introduce a simplified reinforcement learning (RL)-based approach using proximal policy optimization (PPO) to strengthen the ability to identify unsafe concepts from images. Our approach uses reward scores based directly on VLM responses, bypassing the need for collecting human-annotated preference data to train a new reward model. Experimental results show that our approach effectively enhances VLM alignment on images while preserving general capabilities. It outperforms baselines such as supervised fine-tuning (SFT) and direct preference optimization (DPO). We hope our dataset, evaluation findings, and proposed alignment solution contribute to the community's efforts in advancing safe VLMs.