SafetyPairs: Isolating Safety Critical Image Features with Counterfactual Image Generation
This addresses the challenge of fine-grained image safety evaluation for vision-language models, though it is incremental as it builds on existing image editing methods.
The authors tackled the problem of ambiguous image safety labels by introducing SafetyPairs, a framework that generates counterfactual image pairs differing only in safety-critical features to flip safety labels, resulting in a benchmark of over 3,020 images across 9 categories that improves guard model training efficiency.
What exactly makes a particular image unsafe? Systematically differentiating between benign and problematic images is a challenging problem, as subtle changes to an image, such as an insulting gesture or symbol, can drastically alter its safety implications. However, existing image safety datasets are coarse and ambiguous, offering only broad safety labels without isolating the specific features that drive these differences. We introduce SafetyPairs, a scalable framework for generating counterfactual pairs of images, that differ only in the features relevant to the given safety policy, thus flipping their safety label. By leveraging image editing models, we make targeted changes to images that alter their safety labels while leaving safety-irrelevant details unchanged. Using SafetyPairs, we construct a new safety benchmark, which serves as a powerful source of evaluation data that highlights weaknesses in vision-language models' abilities to distinguish between subtly different images. Beyond evaluation, we find our pipeline serves as an effective data augmentation strategy that improves the sample efficiency of training lightweight guard models. We release a benchmark containing over 3,020 SafetyPair images spanning a diverse taxonomy of 9 safety categories, providing the first systematic resource for studying fine-grained image safety distinctions.