  There is great potential for damage from adversarial learning (AL) attacks on
machine-learning based systems. In this paper, we provide a contemporary survey
of AL, focused particularly on defenses against attacks on statistical
classifiers. After introducing relevant terminology and the goals and range of
possible knowledge of both attackers and defenders, we survey recent work on
test-time evasion (TTE), data poisoning (DP), and reverse engineering (RE)
attacks and particularly defenses against same. In so doing, we distinguish
robust classification from anomaly detection (AD), unsupervised from
supervised, and statistical hypothesis-based defenses from ones that do not
have an explicit null (no attack) hypothesis; we identify the hyperparameters a
particular method requires, its computational complexity, as well as the
performance measures on which it was evaluated and the obtained quality. We
then dig deeper, providing novel insights that challenge conventional AL wisdom
and that target unresolved issues, including: 1) robust classification versus
AD as a defense strategy; 2) the belief that attack success increases with
attack strength, which ignores susceptibility to AD; 3) small perturbations for
test-time evasion attacks: a fallacy or a requirement?; 4) validity of the
universal assumption that a TTE attacker knows the ground-truth class for the
example to be attacked; 5) black, grey, or white box attacks as the standard
for defense evaluation; 6) susceptibility of query-based RE to an AD defense.
We also discuss attacks on the privacy of training data. We then present
benchmark comparisons of several defenses against TTE, RE, and backdoor DP
attacks on images. The paper concludes with a discussion of future work.
