Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models

23 April 2024

Papers citing "Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models"

9 / 9 papers shown

Title
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis Songping Wang Yueming Lyu Shiqi Liu Ning Li Tong Tong Hao Sun Caifeng Shan PICV 67 0 0 16 Apr 2025
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models Ziqiang Li Jun Li Lizhi Xiong Zhangjie Fu Zechao Li VLM 59 0 0 17 Mar 2025
AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption Joonsung Jeon Woo Jae Kim Suhyeon Ha Sooel Son Sung-eui Yoon DiffM AAML 54 0 0 13 Mar 2025
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Namhyuk Ahn Kiyoon Yoo Wonhyuk Ahn Daesik Kim Seung-Hun Nam AAML WIGM DiffM 87 0 0 16 Dec 2024
Understanding Implosion in Text-to-Image Generative Models Wenxin Ding Cathy Y. Li Shawn Shan Ben Y. Zhao Haitao Zheng 31 0 0 18 Sep 2024
Replication in Visual Diffusion Models: A Survey and Outlook Wenhao Wang Yifan Sun Zongxin Yang Zhengdong Hu Zhentao Tan Yi Yang 84 6 0 07 Jul 2024
FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu Yu Cheng Zhe Gan S. Sun Tom Goldstein Jingjing Liu AAML 223 438 0 25 Sep 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 294 75,834 0 18 May 2015