Disrupting Style Mimicry Attacks on Video Imagery
Josephine Passananti, Stanley Wu, Shawn Shan, Haitao Zheng, Ben Y., Zhao

TL;DR
This paper investigates methods to disrupt style mimicry attacks on videos, proposing a scene-based segmentation framework that enhances protection against adaptive countermeasures, validated through metrics and user studies.
Contribution
Introduces a scene-based video segmentation framework to improve anti-mimicry protection, addressing vulnerabilities of frame-level defenses against adaptive attacks.
Findings
Protection is effective against mimicry on individual frames.
Scene-based segmentation reduces computational costs and improves robustness.
Adaptive countermeasures are less effective against the proposed framework.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Media Forensic Detection
