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- W4302011565 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." @default.
- W4302011565 created "2022-10-06" @default.
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- W4302011565 date "2022-10-02" @default.
- W4302011565 modified "2023-10-14" @default.
- W4302011565 title "Optimization for Robustness Evaluation beyond $ell_p$ Metrics" @default.
- W4302011565 doi "https://doi.org/10.48550/arxiv.2210.00621" @default.
- W4302011565 hasPublicationYear "2022" @default.
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