Transfer of Adversarial Robustness Between Perturbation Types

3 May 2019

Papers citing "Transfer of Adversarial Robustness Between Perturbation Types"

22 / 22 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 144 1 0 08 May 2025
Achievable distributional robustness when the robust risk is only partially identified Julia Kostin Nicola Gnecco Fanny Yang 143 3 0 04 Feb 2025
Estimating the Probabilities of Rare Outputs in Language Models Gabriel Wu Jacob Hilton AAML UQCV 137 3 0 17 Oct 2024
Towards Universal Certified Robustness with Multi-Norm Training Enyi Jiang Gagandeep Singh Gagandeep Singh AAML 134 1 0 03 Oct 2024
On Continuity of Robust and Accurate Classifiers Ramin Barati Reza Safabakhsh Mohammad Rahmati AAML 104 1 0 29 Sep 2023
Regret-Based Defense in Adversarial Reinforcement Learning Roman Belaire Pradeep Varakantham Thanh Nguyen David Lo AAML 38 3 0 14 Feb 2023
On the interplay of adversarial robustness and architecture components: patches, convolution and attention Francesco Croce Matthias Hein 87 6 0 14 Sep 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 38 19 0 03 Mar 2022
Adversarial Robustness against Multiple and Single $l_p$ -Threat Models via Quick Fine-Tuning of Robust Classifiers Francesco Croce Matthias Hein OOD AAML 67 18 0 26 May 2021
Real-time Detection of Practical Universal Adversarial Perturbations Kenneth T. Co Luis Muñoz-González Leslie Kanthan Emil C. Lupu AAML 66 7 0 16 May 2021
On the effectiveness of adversarial training against common corruptions Klim Kireev Maksym Andriushchenko Nicolas Flammarion AAML 72 103 0 03 Mar 2021
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao Bihan Wen Qian Wang AAML 190 496 0 02 Feb 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 345 707 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 128 549 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 86 64 0 16 Jan 2020
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 124 105 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 95 96 0 29 Sep 2019
Adversarial Robustness Against the Union of Multiple Perturbation Models Pratyush Maini Eric Wong J. Zico Kolter OOD AAML 63 151 0 09 Sep 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt Basel Alomair OODD 264 1,485 0 16 Jul 2019
Functional Adversarial Attacks Cassidy Laidlaw Soheil Feizi AAML 100 185 0 29 May 2019
$Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$$ Provable robustness against all adversarial $l_p$ -perturbations for $p\geq 1$ Francesco Croce Matthias Hein OOD 75 75 0 27 May 2019
Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Dan Boneh AAML SILM 96 380 0 30 Apr 2019