Randomness in ML Defenses Helps Persistent Attackers and Hinders
Evaluators

Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators

27 February 2023

Matthew Jagielski

Florian Tramèr

Nicholas Carlini

Papers citing "Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators"

9 / 9 papers shown

Title
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search Zachary Coalson Huazheng Wang Qingyun Wu Sanghyun Hong OOD AAML 34 0 0 09 May 2024
Diffusion Denoising as a Certified Defense against Clean-label Poisoning Sanghyun Hong Nicholas Carlini Alexey Kurakin DiffM 40 3 0 18 Mar 2024
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models Weikang Yu Yonghao Xu Pedram Ghamisi 21 4 0 31 Jul 2023
Are aligned neural networks adversarially aligned? Nicholas Carlini Milad Nasr Christopher A. Choquette-Choo Matthew Jagielski Irena Gao ... Pang Wei Koh Daphne Ippolito Katherine Lee Florian Tramèr Ludwig Schmidt AAML 22 221 0 26 Jun 2023
On the Role of Randomization in Adversarially Robust Classification Lucas Gnecco-Heredia Y. Chevaleyre Benjamin Négrevergne Laurent Meunier Muni Sreenivas Pydi AAML 16 5 0 14 Feb 2023
Diffusion Models for Adversarial Purification Weili Nie Brandon Guo Yujia Huang Chaowei Xiao Arash Vahdat Anima Anandkumar WIGM 195 415 0 16 May 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 217 675 0 19 Oct 2020
Benchmarking Adversarial Robustness Yinpeng Dong Qi-An Fu Xiao Yang Tianyu Pang Hang Su Zihao Xiao Jun Zhu AAML 23 36 0 26 Dec 2019
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples Xiaojun Jia Xingxing Wei Xiaochun Cao H. Foroosh AAML 50 264 0 30 Nov 2018