Simple Post-Training Robustness using Test Time Augmentations and Random Forest

Gilad Cohen*, Raja Giryes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Although Deep Neural Networks (DNNs) achieve excellent performance on many real-world tasks, they are highly vulnerable to adversarial attacks. A leading defense against such attacks is adversarial training, a technique in which a DNN is trained to be robust to adversarial attacks by introducing adversarial noise to its input. This procedure is effective but must be done during the training phase. In this work, we propose Augmented Random Forest (ARF), a simple and easy-to-use strategy for robustifying an existing pretrained DNN without modifying its weights. For every image, we generate randomized test time augmentations by applying diverse color, blur, noise, and geometric transforms. Then we use the DNN's logits output to train a simple random forest to predict the real class label. Our method achieves state-of-the-art adversarial robustness on a diversity of white and black box attacks with minimal compromise on the natural images' classification. We test ARF also against numerous adaptive white-box attacks and it shows excellent results when combined with adversarial training. https://github.com/giladcohen/ARF.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3984-3994
Number of pages11
ISBN (Electronic)9798350318920
DOIs
StatePublished - 2024
Event2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States
Duration: 4 Jan 20248 Jan 2024

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Conference

Conference2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period4/01/248/01/24

Keywords

  • Adversarial learning
  • Algorithms
  • adversarial attack and defense methods

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