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Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
May 3, 2024, 4:59 a.m. | Takami Sato, Justin Yue, Nanze Chen, Ningfei Wang, Qi Alfred Chen
cs.CV updates on arXiv.org arxiv.org
Abstract: Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. However, we find that this technology is actually a double-edged sword: We identify a new type of attack, called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still …
abstract applications arxiv capability cs.cr cs.cv denoising diffusion diffusion models gans generate generative generative models human illustrations image images natural performance photo prior study text text-to-image type
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