Transferable Universal Adversarial Perturbations Using Generative Models

28 October 2020

Papers citing "Transferable Universal Adversarial Perturbations Using Generative Models"

9 / 9 papers shown

Title
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models Hao Fang Jiawei Kong Wenbo Yu Bin Chen Jiawei Li Hao Wu Ke Xu Ke Xu AAML VLM 40 13 0 08 Jun 2024
Universal Adversarial Directions Ching Lam Choi Farzan Farnia AAML 9 0 0 28 Oct 2022
A Human-in-the-Middle Attack against Object Detection Systems Han-Ching Wu Sareh Rowlands Johan Wahlstrom AAML 23 0 0 15 Aug 2022
Investigating Top- $k$ White-Box and Transferable Black-box Attack Chaoning Zhang Philipp Benz Adil Karjauv Jae-Won Cho Kang Zhang In So Kweon 31 42 0 30 Mar 2022
Robust Feature-Level Adversaries are Interpretability Tools Stephen Casper Max Nadeau Dylan Hadfield-Menell Gabriel Kreiman AAML 42 27 0 07 Oct 2021
A Survey On Universal Adversarial Attack Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu In So Kweon AAML 23 90 0 02 Mar 2021
Transferable Adversarial Attacks for Image and Video Object Detection Xingxing Wei Siyuan Liang Ning Chen Xiaochun Cao AAML 74 221 0 30 Nov 2018
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 185 302 0 21 May 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,835 0 08 Jul 2016