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Robustness Out of the Box: Compositional Representations Naturally
  Defend Against Black-Box Patch Attacks

Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks

1 December 2020
Christian Cosgrove
Adam Kortylewski
Chenglin Yang
Alan Yuille
    AAML
ArXiv (abs)PDFHTML

Papers citing "Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks"

3 / 3 papers shown
Title
PatchCURE: Improving Certifiable Robustness, Model Utility, and
  Computation Efficiency of Adversarial Patch Defenses
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
Chong Xiang
Tong Wu
Sihui Dai
Jonathan Petit
Suman Jana
Prateek Mittal
122
6
0
19 Oct 2023
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Abhijith Sharma
Yijun Bian
Phil Munz
Apurva Narayan
VLMAAML
82
20
0
16 Jun 2022
PatchCleanser: Certifiably Robust Defense against Adversarial Patches
  for Any Image Classifier
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Chong Xiang
Saeed Mahloujifar
Prateek Mittal
VLMAAML
103
77
0
20 Aug 2021
1