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20222026
most citedAdversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

19 citations · 34 across the 10 of their papers we have counts for

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cs.CV2024

CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion

Xiaoyu Wu, Yang Hua, Chumeng Liang +4

Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot generation where a pretrained model is fine-tuned on a small set of images to capt…

cs.CV2023

Toward effective protection against diffusion based mimicry through score distillation

Haotian Xue, Chumeng Liang, Xiaoyu Wu +1

While generative diffusion models excel in producing high-quality images, they can also be misused to mimic authorized images, posing a significant threat to AI systems. Efforts ha…

cs.CV2023★ 4 cited

Targeted Attack Improves Protection against Unauthorized Diffusion Customization

Boyang Zheng, Chumeng Liang, Xiaoyu Wu

Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on ad…

cs.CV2023★ 9 cited

Mist: Towards Improved Adversarial Examples for Diffusion Models

Chumeng Liang, Xiaoyu Wu

Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual propertie…

cs.CV2023★ 19 cited

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Chumeng Liang, Xiaoyu Wu, Yang Hua +6

Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate no…