Toward Robust Imperceptible Perturbation against Unauthorized Text-to-image Diffusion-based Synthesis. (arXiv:2311.13127v1 [cs.CV])

Text-to-image diffusion models allow seamless generation of personalized
images from scant reference photos. Yet, these tools, in the wrong hands, can
fabricate misleading or harmful content, endangering individuals. To address
this problem, existing poisoning-based approaches perturb user images in an
imperceptible way to render them "unlearnable" from malicious uses. We identify
two limitations of these defending approaches: i) sub-optimal due to the
hand-crafted heuristics for solving the intractable bilevel optimization and
ii) lack of robustness against simple data transformations like …

address diffusion models image images malicious photos poisoning problem reference text tools wrong