Explore detailed results of user evaluations on style mimicry quality and fit, broken down per artist and method, with preference ratings for each scenario.

Explore how methods like DiffPure, Noisy Upscaling, and IMPRESS++ weaken style mimicry protections in AI-generated art with low-effort techniques.

Learn about existing protections against style mimicry in AI art, including encoder and denoiser-based defenses like Glaze, Mist, and Anti-DreamBooth.

Discover the design and methodology behind our user study evaluating robust style mimicry methods, including quality and style assessments using Amazon MTurk.

Review the compute resources used for our style mimicry experiments, detailing execution times and additional resource requirements for various methods.

Explore the experimental setup and hyperparameters used for evaluating robust style mimicry methods, including Stable Diffusion, Glaze, Anti-DreamBooth, & more.