Certified Patch Robustness via Smoothed Vision Transformers

Author:

Salman Hadi1,Jain Saachi1,Wong Eric1,Madry Aleksander1

Affiliation:

1. MIT

Funder

NSF

Defense Advanced Research Projects Agency (DARPA)

Publisher

IEEE

Reference63 articles.

1. Adversarial training against location-optimized adversarial patches;rao;ArXiv Preprint,2020

2. Attacking Optical Flow

3. Differentiable abstract interpretation for provably robust neural networks;mirman;International Conference on Machine Learning (ICML),0

4. Efficient certified defenses against patch attacks on image classifiers;metzen;International Conference on Learning Representations,0

5. An approach to reachability analysis for feed-forward relu neural networks;lomuscio;ArXiv Preprint,2017

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1. Defending Against Local Adversarial Attacks through Empirical Gradient Optimization;Tehnicki vjesnik - Technical Gazette;2023-12-15

2. A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

3. Defending Against Universal Patch Attacks by Restricting Token Attention in Vision Transformers;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

4. Jedi: Entropy-Based Localization and Removal of Adversarial Patches;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

5. ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking;2023 IEEE Symposium on Security and Privacy (SP);2023-05

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