Remote associations play a crucial role in enhancing creativity during design ideation, yet designers face challenges in effectively creating and integrating them. Our formative study (N = 5) shows the potential of AI-generated remote associations to facilitate this process, but there are still challenges to understand and apply them. To address these, we propose StarBurst, which supports design ideation through AI-generated remote associations. It consists of three core components: (1) generating diverse remote associations from images to expand creative possibilities; (2) constructing an attribute map to explore connections between associative elements; and (3) providing suggestions to integrate these associations into the final design idea. Through two forms of user studies (N = 32, N = 16), we found that StarBurst outperformed designers in generating remote associations and provided more effective support for diverse and creative idea development compared to the baseline system. Additionally, we discussed how users’ usage patterns and perceptions influence StarBurst’s effectiveness.

In the era of widespread digital art dissemination, visible watermarks provide immediate copyright identification by overlaying visible markers, addressing the lag issue of invisible watermarks that are difficult to prevent in advance due to post hoc evidence collection, thus meeting artists' needs for preemptive prevention and explicit protection. However, existing approaches struggle to balance aesthetics and functionality. To address this challenge, we conducted an exploratory study with watermarking experts, identifying key principles, six common design patterns, and a systematic watermarking workflow. Based on these insights, we developed an end-to-end, perceptual-aware framework for aesthetic-preserving watermark embedding, modeled after expert workflows in 5 phases. Using the Chain-of-Thought strategy, we optimized prompt instructions to guide the Vision-Language Model in emulating experts' decision-making, generating effective watermarking schemes and conducting objective visual evaluations. Iterative feedback optimization ensures watermarked images adhere to aesthetic principles. Quantitative and qualitative experiments demonstrate the system's superiority over baseline methods in preserving aesthetics and ensuring effective copyright protection.