SafetyPairs: Isolating Safety Critical Image Features with Counterfactual Image Generation
Apple Machine Learning Journal / 3/24/2026
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Key Points
- The paper introduces SafetyPairs, a method for isolating safety-critical visual features in images by using counterfactual image generation.
- It focuses on identifying which image components drive safety-relevant model behavior, aiming to improve interpretability and robustness in computer vision systems.
- The approach is presented as a research contribution from an ICLR Workshop context, with authors spanning multiple institutions.
- The work targets safer image understanding pipelines by separating features associated with “safe” vs. “unsafe” outcomes rather than treating all visual evidence as equally important.
This paper was accepted at the Principled Design for Trustworthy AI — Interpretability, Robustness, and Safety across Modalities Workshop at ICLR 2026.
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…
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