The field of generative models is rapidly evolving, with a growing focus on addressing the legal and ethical challenges associated with copyright infringement. Researchers are developing innovative methods to mitigate copyright risks, including prompt-based strategies, attention-based similarity analysis, and adaptive mitigation techniques. These approaches aim to balance the need for creative freedom with the requirement to protect intellectual property rights. Notable papers in this area include AMCR, which introduces a comprehensive framework for assessing and mitigating copyright risks, and PromptCOS, which proposes a method for auditing prompt copyright based on content-level output similarity. Other significant contributions include SuMa, a subspace mapping approach for robust and effective concept erasure in text-to-image diffusion models, and No Encore, which explores the application of machine unlearning techniques to prevent inadvertent usage of creative content. These advancements have the potential to enable the safer deployment of generative models and promote more responsible innovation in the field.