FVH-Bench 论文提出超越显性有害内容评估图像生成器安全
FVH-Bench: Evaluating Image Generator Safety Beyond Overtly Harmful Imagery
AI 导读
研究团队提出功能性视觉伤害（FVH）概念，指图像生成器虽拒绝显性有害请求，仍可能生成指导危险行为或支持欺骗的图表、界面等信息类产物。新基准 FVH-Bench 包含 669 条提示词，覆盖五类有害视觉功能和十个风险领域，测试的 11 个图像生成器很少拒绝此类请求，现有安全检测器对 FVH 的覆盖也有限。
正文 · 原文
Abstract:Modern image generators can produce complex visual artifacts, including diagrams, interfaces, and records that combine text, graphics, and layout to convey detailed information. They may refuse requests for overtly harmful imagery yet still generate artifacts that guide dangerous actions or support deception. We call the risk arising from what such images enable, rather than only what they depict, functional visual harm (FVH). Existing safety benchmarks focus largely on overtly harmful content, such as pornography and graphic violence, leaving image generators' safety alignment against FVH insufficiently evaluated. To address this gap, we introduce FVH-Bench, comprising 669 prompts across five harmful visual functions and ten risk domains. The 11 evaluated image generators rarely refuse these requests and frequently generate artifacts that fulfill the targeted harmful functions. High scores for target harm realization and harmful artifact usability further indicate that these outputs can support the intended harmful purposes. A further analysis finds that existing safety detectors also provide limited coverage of FVH, indicating gaps in their ability to recognize harmful visual functions. Together, these findings show that evaluating image-generator safety alignment requires considering not only what images depict, but also what they may enable.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37722 [cs.CR] |
| (or arXiv:2610.37722v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37722 arXiv-issued DOI via DataCite |
Submission history
From: Zihao Zhu [view email]
[v1]
Tue, 27 Oct 2026 10:35:58 UTC (21,350 KB)
来源：arXiv cs.AI · arxiv.org