  Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial
examples, which are slightly perturbed input images which lead DNNs to make
wrong predictions. To protect from such examples, various defense strategies
have been proposed. A very recent defense strategy for detecting adversarial
examples, that has been shown to be robust to current attacks, is to check for
intrinsic context consistencies in the input data, where context refers to
various relationships (e.g., object-to-object co-occurrence relationships) in
images. In this paper, we show that even context consistency checks can be
brittle to properly crafted adversarial examples and to the best of our
knowledge, we are the first to do so. Specifically, we propose an adaptive
framework to generate examples that subvert such defenses, namely, Adversarial
attacks against object Detection that evade Context consistency checks (ADC).
In ADC, we formulate a joint optimization problem which has two attack goals,
viz., (i) fooling the object detector and (ii) evading the context consistency
check system, at the same time. Experiments on both PASCAL VOC and MS COCO
datasets show that examples generated with ADC fool the object detector with a
success rate of over 85% in most cases, and at the same time evade the recently
proposed context consistency checks, with a bypassing rate of over 80% in most
cases. Our results suggest that how to robustly model context and check its
consistency, is still an open problem.
