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.
This paper introduces MarginAttack, a stronger and faster zero-confidence adversarial attack.