Optimization for Robustness Evaluation Beyond ℓp Metrics

Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, Ju Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Empirical evaluation of the adversarial robustness of deep learning models involves solving non-trivial constrained optimization problems. Popular numerical algorithms to solve these constrained problems rely predominantly on projected gradient descent (PGD) and mostly handle adversarial perturbations modeled by the ℓ1, ℓ2, and ℓ metrics. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO, With Constraint-Folding (PWCF), to add reliability and generality to robustness evaluation. PWCF 1) finds good-quality solutions without the need of delicate hyperparameter tuning and 2) can handle more general perturbation types, e.g., modeled by general ℓp (where p > 0) and perceptual (nonℓp) distances, which are inaccessible to existing PGD-based algorithms.

Original languageEnglish (US)
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: Jun 4 2023Jun 10 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period6/4/236/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • adversarial attack
  • adversarial robustness
  • constrained optimization
  • deep neural networks
  • perceptual distance
  • robustness evaluation

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