  Deep neural networks (DNNs) are vulnerable to adversarial examples,
perturbations carefully crafted to fool the targeted DNN, in both the
non-targeted and targeted case. In the non-targeted case, the attacker simply
aims to induce misclassification. In the targeted case, the attacker aims to
induce classification to a specified target class. In addition, it has been
observed that strong adversarial examples can transfer to unknown models,
yielding a serious security concern. The NIPS 2017 competition was organized to
accelerate research in adversarial attacks and defenses, taking place in the
realistic setting where submitted adversarial attacks attempt to transfer to
submitted defenses. The CAAD 2018 competition took place with nearly identical
rules to the NIPS 2017 one. Given the requirement that the NIPS 2017
submissions were to be open-sourced, participants in the CAAD 2018 competition
were able to directly build upon previous solutions, and thus improve the
state-of-the-art in this setting. Our team participated in the CAAD 2018
competition, and won 1st place in both attack subtracks, non-targeted and
targeted adversarial attacks, and 3rd place in defense. We outline our
solutions and development results in this article. We hope our results can
inform researchers in both generating and defending against adversarial
examples.
