  Robust machine learning is currently one of the most prominent topics which
could potentially help shaping a future of advanced AI platforms that not only
perform well in average cases but also in worst cases or adverse situations.
Despite the long-term vision, however, existing studies on black-box
adversarial attacks are still restricted to very specific settings of threat
models (e.g., single distortion metric and restrictive assumption on target
model's feedback to queries) and/or suffer from prohibitively high query
complexity. To push for further advances in this field, we introduce a general
framework based on an operator splitting method, the alternating direction
method of multipliers (ADMM) to devise efficient, robust black-box attacks that
work with various distortion metrics and feedback settings without incurring
high query complexity. Due to the black-box nature of the threat model, the
proposed ADMM solution framework is integrated with zeroth-order (ZO)
optimization and Bayesian optimization (BO), and thus is applicable to the
gradient-free regime. This results in two new black-box adversarial attack
generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image
classification datasets show that our proposed approaches have much lower
function query complexities compared to state-of-the-art attack methods, but
achieve very competitive attack success rates.
