Recent studies show that image and video generation models can be prompted to reproduce copyrighted content (e.g., copyrighted characters) from their training data, raising serious legal concerns about copyright infringement. We systematically evaluate the issue. First, we build COPYCAT, an evaluation suite consisting of diverse copyrighted characters and an evaluation pipeline that considers both the detection of similarity to copyrighted characters and the generated image’s consistency with user input. Both image and video generation models can still generate characters even if characters’ names are not explicitly mentioned in the prompt, sometimes with only two generic keywords (e.g., prompting with “videogame, plumber” consistently generates Nintendo’s Mario character). We then introduce techniques to semi-automatically identify such keywords or descriptions that trigger character generation. We also find that commonly employed mitigation strategies, such as prompt rewriting in the DALL·E system, are not fully effective as standalone guardrails. These strategies must be coupled with other approaches, like negative prompting, to effectively reduce the unintended generation of copyrighted characters. Our work provides empirical grounding to the discussion of copyright mitigation strategies and offers actionable insights for model deployers actively implementing them.