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A Majority Invariant Approach to Patch Robustness Certification for Deep
  Learning Models

A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models

1 August 2023
Qili Zhou
Zhengyuan Wei
Haipeng Wang
William Chan
    AAML
ArXivPDFHTML

Papers citing "A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models"

3 / 3 papers shown
Title
PatchCensor: Patch Robustness Certification for Transformers via
  Exhaustive Testing
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing
Yuheng Huang
L. Ma
Yuanchun Li
ViT
AAML
28
8
0
19 Nov 2021
Certified Patch Robustness via Smoothed Vision Transformers
Certified Patch Robustness via Smoothed Vision Transformers
Hadi Salman
Saachi Jain
Eric Wong
Aleksander Mkadry
AAML
62
58
0
11 Oct 2021
PatchGuard++: Efficient Provable Attack Detection against Adversarial
  Patches
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches
Chong Xiang
Prateek Mittal
AAML
31
42
0
26 Apr 2021
1