Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
For developers and policymakers, this work quantifies the inadequacy of current IP guardrails in widely used image generation models.
This report presents a benchmark and automated pipeline to evaluate IP guardrails in generative image models. Testing 14 models, they found that while private models refuse some IP-related generations, all models readily generated recognizable IP as of March 2026, with commercial logos refused least frequently.
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.