  There has been a recent surge in research on adversarial perturbations that
defeat Deep Neural Networks (DNNs) in machine vision; most of these
perturbation-based attacks target object classifiers. Inspired by the
observation that humans are able to recognize objects that appear out of place
in a scene or along with other unlikely objects, we augment the DNN with a
system that learns context consistency rules during training and checks for the
violations of the same during testing. Our approach builds a set of
auto-encoders, one for each object class, appropriately trained so as to output
a discrepancy between the input and output if an added adversarial perturbation
violates context consistency rules. Experiments on PASCAL VOC and MS COCO show
that our method effectively detects various adversarial attacks and achieves
high ROC-AUC (over 0.95 in most cases); this corresponds to over 20%
improvement over a state-of-the-art context-agnostic method.
