Anti-Reference: Universal and Immediate Defense Against Reference-Based Generation
Diffusion models have revolutionized generative modeling with their exceptional ability to produce high-fidelity images. However, misuse of such potent tools can lead to the creation of fake news or disturbing content targeting individuals, resulting in significant social harm. In this paper, we int...
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Zusammenfassung: | Diffusion models have revolutionized generative modeling with their
exceptional ability to produce high-fidelity images. However, misuse of such
potent tools can lead to the creation of fake news or disturbing content
targeting individuals, resulting in significant social harm. In this paper, we
introduce Anti-Reference, a novel method that protects images from the threats
posed by reference-based generation techniques by adding imperceptible
adversarial noise to the images. We propose a unified loss function that
enables joint attacks on fine-tuning-based customization methods,
non-fine-tuning customization methods, and human-centric driving methods. Based
on this loss, we train a Adversarial Noise Encoder to predict the noise or
directly optimize the noise using the PGD method. Our method shows certain
transfer attack capabilities, effectively challenging both gray-box models and
some commercial APIs. Extensive experiments validate the performance of
Anti-Reference, establishing a new benchmark in image security. |
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DOI: | 10.48550/arxiv.2412.05980 |