Optimization for Robustness Evaluation beyond $\ell_p$ Metrics

• URL: http://arxiv.org/abs/2210.00621v1
• Date: Sun, 2 Oct 2022 20:48:05 GMT
• Title: Optimization for Robustness Evaluation beyond $\ell_p$ Metrics
• Authors: Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, Ju Sun
• Abstract summary: Empirical evaluation of deep learning models against adversarial attacks involves solving nontrivial constrained optimization problems. We introduce a novel framework that blends a general-purpose constrained-optimization solver PyGRANSO, With Constraint-Folding (PWCF) to add reliability and generality to robustness evaluation.
• Score: 11.028091609739738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems. Popular algorithms for solving these constrained problems rely on projected gradient descent (PGD) and require careful tuning of multiple hyperparameters. Moreover, PGD can only handle $\ell_1$, $\ell_2$, and $\ell_\infty$ attack models due to the use of analytical projectors. 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 general attack models, e.g., general $\ell_p$ ($p \geq 0$) and perceptual attacks, which are inaccessible to PGD-based algorithms.

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