The robust mimicry methods discussed reveal how simple, low-effort techniques can substantially undermine existing style mimicry protections, focusing on weakening defenses rather than maximizing performance.
In our findings, while upscaling typically helps purify adversarially perturbed images in classifier settings, it proves ineffective against generative models, highlighting a critical gap in current protective strategies.
The modifications made to the IMPRESS algorithm, such as changing the loss function and introducing negative prompting, enhance its capability to handle style mimicry threats effectively.
Mimicry methods like unconditional DiffPure can exploit the characteristics of text-to-image models, showcasing how various approaches can significantly reduce the efficacy of existing protections.
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