Title: An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack. Abstract: There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations.  The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level.  The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause misclassification, also known as the margin of an input feature.  While the former paradigm is well-resolved, the latter is not.  Existing zero-confidence attacks either introduce significant approximation errors, or are too time-consuming.  We therefore propose MarginAttack, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency.  Our experiments show that MarginAttack is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation attacks.  In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.