Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report

A technical report evaluating 14 text-to-image models found that while all private models have some IP guardrails, refusal rates vary widely and all models readily generated images containing recognizable intellectual property as of March 2026. Commercial logos were refused least frequently and generated at the highest rate.

arXiv:2607.02582v1 Announce Type: new Abstract: Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property IP . In this technical report, we present a benchmark and automated evaluation pipeline to test for evidence of IP guardrails in generative image models along with the propensity for these models to generate images with recognizable IP. The IP categories we tested include fictional characters, celebrity likeness, and commercial logos and do not encompass the full range of IP which may be implicated by image generation models. We evaluated fourteen widely used text-to-image models, including three self-hosted open weights models and eleven private models. While all of the private models were observed to refuse generations at some level due to IP guardrails, the frequency of generation refusals varied substantially among models. The refusal rates also varied considerably across the different IP categories tested. Commercial logos were refused least frequently and were successfully generated at the highest rate, on average. Though the rate varies, all models tested readily generated images containing recognizable IP as of March 2026.