  Recently, adversarial attack methods have been developed to challenge the
robustness of machine learning models. However, mainstream evaluation criteria
experience limitations, even yielding discrepancies among results under
different settings. By examining various attack algorithms, including
gradient-based and query-based attacks, we notice the lack of a consensus on a
uniform standard for unbiased performance evaluation. Accordingly, we propose a
Piece-wise Sampling Curving (PSC) toolkit to effectively address the
aforementioned discrepancy, by generating a comprehensive comparison among
adversaries in a given range. In addition, the PSC toolkit offers options for
balancing the computational cost and evaluation effectiveness. Experimental
results demonstrate our PSC toolkit presents comprehensive comparisons of
attack algorithms, significantly reducing discrepancies in practice.
