  Generative AI models are often used to perform mimicry attacks, where a
pretrained model is fine-tuned on a small sample of images to learn to mimic a
specific artist of interest. While researchers have introduced multiple
anti-mimicry protection tools (Mist, Glaze, Anti-Dreambooth), recent evidence
points to a growing trend of mimicry models using videos as sources of training
data. This paper presents our experiences exploring techniques to disrupt style
mimicry on video imagery. We first validate that mimicry attacks can succeed by
training on individual frames extracted from videos. We show that while
anti-mimicry tools can offer protection when applied to individual frames, this
approach is vulnerable to an adaptive countermeasure that removes protection by
exploiting randomness in optimization results of consecutive (nearly-identical)
frames. We develop a new, tool-agnostic framework that segments videos into
short scenes based on frame-level similarity, and use a per-scene optimization
baseline to remove inter-frame randomization while reducing computational cost.
We show via both image level metrics and an end-to-end user study that the
resulting protection restores protection against mimicry (including the
countermeasure). Finally, we develop another adaptive countermeasure and find
that it falls short against our framework.
