  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.
