Transfer of Adversarial Robustness Between Perturbation Types

Transfer of Adversarial Robustness Between Perturbation Types

3 May 2019

Jacob Steinhardt

Papers citing "Transfer of Adversarial Robustness Between Perturbation Types"

19 / 19 papers shown

Title
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP Hanxun Huang Sarah Monazam Erfani Yige Li Xingjun Ma James Bailey AAML 53 0 0 08 May 2025
Achievable distributional robustness when the robust risk is only partially identified Julia Kostin Nicola Gnecco Fanny Yang 73 3 0 04 Feb 2025
Estimating the Probabilities of Rare Outputs in Language Models Gabriel Wu Jacob Hilton AAML UQCV 48 2 0 17 Oct 2024
Towards Universal Certified Robustness with Multi-Norm Training Enyi Jiang Gagandeep Singh Gagandeep Singh AAML 60 1 0 03 Oct 2024
RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations Enyi Jiang Gagandeep Singh AAML 30 1 0 09 Feb 2024
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck Jongheon Jeong Sihyun Yu Hankook Lee Jinwoo Shin AAML 44 0 0 24 Mar 2023
Regret-Based Defense in Adversarial Reinforcement Learning Roman Belaire Pradeep Varakantham Thanh Nguyen David Lo AAML 23 3 0 14 Feb 2023
Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges K. T. Baghaei Amirreza Payandeh Pooya Fayyazsanavi Shahram Rahimi Zhiqian Chen Somayeh Bakhtiari Ramezani FaML AI4TS 38 6 0 27 Nov 2022
On the interplay of adversarial robustness and architecture components: patches, convolution and attention Francesco Croce Matthias Hein 43 6 0 14 Sep 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 13 18 0 03 Mar 2022
Real-time Detection of Practical Universal Adversarial Perturbations Kenneth T. Co Luis Muñoz-González Leslie Kanthan Emil C. Lupu AAML 33 6 0 16 May 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 680 0 19 Oct 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 33 536 0 01 Jul 2020
A simple way to make neural networks robust against diverse image corruptions E. Rusak Lukas Schott Roland S. Zimmermann Julian Bitterwolf Oliver Bringmann Matthias Bethge Wieland Brendel 21 64 0 16 Jan 2020
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 18 104 0 13 Nov 2019
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts Yu Sun Xiaolong Wang Zhuang Liu John Miller Alexei A. Efros Moritz Hardt TTA OOD 27 92 0 29 Sep 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt D. Song OODD 89 1,426 0 16 Jul 2019
Functional Adversarial Attacks Cassidy Laidlaw S. Feizi AAML 19 183 0 29 May 2019
Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Dan Boneh AAML SILM 28 375 0 30 Apr 2019