测试14个图像生成模型的版权防护机制，发现拒生成率差异大且仍常产出可识别版权内容。
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
- 构建自动评估流程，测试模型对虚构角色、名人脸型、商业标识等版权内容的防护能力。
- 所有私有模型均有拒生成行为，但拒止率在不同版权类型间差异显著，商业标识最易生成。
- 截至2026年3月，所有模型均能轻易生成带有可识别版权特征的图像，防护效果有限。
生成式图像模型能够生成与已知知识产权（IP）高度相似或复制的图像。本技术报告提出一个基准测试和自动化评估流程，用于检测生成式图像模型中是否存在知识产权防护机制，并评估其生成可识别知识产权图像的倾向。我们测试的知识产权类别包括虚构角色、名人外貌相似性以及商业标志，未涵盖图像生成可能涉及的全部知识产权类型。我们评估了14个广泛使用的文本到图像模型，包括3个自托管开源权重模型和11个私有模型。尽管所有私有模型在一定程度上表现出因知识产权防护机制而拒绝生成的行为，但拒绝频率在不同模型间存在显著差异。拒绝率在不同知识产权类别间也变化较大，其中商业标志被拒绝的频率最低，生成成功率最高。尽管各模型拒绝率存在差异，但截至2026年3月，所有测试模型均能轻松生成包含可识别知识产权的图像。
原文摘要 · Abstract (English)
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.
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