VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
The rapid advancement of vision-language models (VLMs) has brought a lot of
attention to their safety alignment. However, existing methods have primarily
focused on model undersafety, where the model responds to hazardous queries,
while neglecting oversafety, where the model refuses to answer safe queries. In
this paper, we introduce the concept of $\textit{safety calibration}$, which
systematically addresses both undersafety and oversafety. Specifically, we
present $\textbf{VSCBench}$, a novel dataset of 3,600 image-text pairs that are
visually or textually similar but differ in terms of safety, which is designed
to evaluate safety calibration across image-centric and text-centric scenarios.
Based on our benchmark, we evaluate safety calibration across eleven widely
used VLMs. Our extensive experiments revealed major issues with both
undersafety and oversafety. We further investigated four approaches to improve
the model's safety calibration. We found that even though some methods
effectively calibrated the models' safety problems, these methods also lead to
the degradation of models' utility. This trade-off underscores the urgent need
for advanced calibration methods, and our benchmark provides a valuable tool
for evaluating future approaches. Our code and data are available at
https://github.com/jiahuigeng/VSCBench.git.
