Generating Risky Samples with Conformity Constraints via Diffusion Models

This article likely discusses a novel approach to generating data samples using diffusion models, with a focus on controlling the characteristics of the generated samples, specifically to include risky or potentially problematic content while adhering to certain constraints. The use of 'conformity constraints' suggests a mechanism to ensure the generated samples meet specific criteria, possibly related to safety, ethics, or other regulations. The research likely explores the challenges and potential applications of this technique.