arXiv 论文研究视觉语言模型如何通过图像变换编码种族与性取向等敏感属性
Physiognomy Revisited: How Vision-Language Models Encode Sensitive Attributes Through Image Transformation
AI 导读
论文考察两款知名视觉语言模型在被要求将人脸图像变换为特定种族或性取向时的行为，发现模型常会照做，且服从与拒绝非确定、具群体敏感性，生成图像统计上可区分并放大刻板印象。研究还发现图像生成引入的表征偏差会重新出现在文本推理任务中，并涉及犯罪相关偏见，提示 AI 辅助画像的风险。论文已被 EMNLP 2026 主会接收。
正文 · 原文
Abstract:Emergent generative AI technologies, while remarkably beneficial across several tasks and domains, can suffer from unforeseen vulnerabilities and difficult-to-anticipate failure modes. Such failure modes can disparately affect vulnerable communities. This paper investigates two well-known vision language models (VLMs) and inspects how these models encode protected identities while generating images. We show that when VLMs are requested to transform human face images to reflect race identities (e.g., Hispanic, Black, White, or Asian) and sexual orientation (e.g., gay or straight), VLMs often comply with such requests. We demonstrate that compliance (and refusal) is non-deterministic, group-sensitive, and generated images are statistically distinguishable, often accentuating deep-seated stereotypes. We further show that representation biases introduced during image generation can re-emerge in textual reasoning and characterization tasks. Finally, we show that this worrisome finding is not limited to racial identity or sexual orientation, image generation can perpetuate bias around criminality, raising concerning possibilities of AI-assisted profiling.
| Comments: | Accepted at EMNLP 2026 (Main Conference Track) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.35910 [cs.CV] |
| (or arXiv:2610.35910v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35910 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ashiqur Rahman KhudaBukhsh [view email]
[v1]
Tue, 6 Oct 2026 17:00:47 UTC (44,868 KB)
来源：arXiv cs.AI · arxiv.org