ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized Text-to-Image Generation
Qianrui Teng, Xing Cui, Xuannan Liu, Peipei Li, Zekun Li, Huaibo, Huang, Ran He

TL;DR
This paper introduces ID-Cloak, a novel method for creating identity-specific privacy cloaks that protect all images of a person from personalized text-to-image generation, addressing practical limitations of existing techniques.
Contribution
The paper proposes the first approach to generate universal, identity-specific cloaks that safeguard all images of an individual against personalized image synthesis models.
Findings
The identity-specific cloaks effectively prevent personalized image generation.
The method generalizes across diverse personal images and contexts.
Experiments demonstrate significant privacy protection with the proposed cloaks.
Abstract
Personalized text-to-image models allow users to generate images of new concepts from several reference photos, thereby leading to critical concerns regarding civil privacy. Although several anti-personalization techniques have been developed, these methods typically assume that defenders can afford to design a privacy cloak corresponding to each specific image. However, due to extensive personal images shared online, image-specific methods are limited by real-world practical applications. To address this issue, we are the first to investigate the creation of identity-specific cloaks (ID-Cloak) that safeguard all images belong to a specific identity. Specifically, we first model an identity subspace that preserves personal commonalities and learns diverse contexts to capture the image distribution to be protected. Then, we craft identity-specific cloaks with the proposed novel objective…
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Taxonomy
TopicsDigital Humanities and Scholarship · Topic Modeling · Natural Language Processing Techniques
