Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

17 June 2024

Nicholas Carlini

Florian Tramèr

Papers citing "Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI"

6 / 6 papers shown

Title
An Adversarial Perspective on Machine Unlearning for AI Safety Jakub Łucki Boyi Wei Yangsibo Huang Peter Henderson F. Tramèr Javier Rando MU AAML 66 31 0 26 Sep 2024
Dormant: Defending against Pose-driven Human Image Animation Jiachen Zhou Mingsi Wang Tianlin Li Guozhu Meng Kai Chen 44 3 0 22 Sep 2024
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion Anton Razzhigaev Arseniy Shakhmatov Anastasia Maltseva V.Ya. Arkhipkin Igor Pavlov Ilya Ryabov Angelina Kuts Alexander Panchenko Andrey Kuznetsov Denis Dimitrov 37 76 0 05 Oct 2023
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li Dongxu Li Silvio Savarese Steven C. H. Hoi VLM MLLM 244 4,186 0 30 Jan 2023
Diffusion Models for Adversarial Purification Weili Nie Brandon Guo Yujia Huang Chaowei Xiao Arash Vahdat Anima Anandkumar WIGM 187 410 0 16 May 2022
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 136 189 0 13 Jan 2021