  Vision systems that deploy Deep Neural Networks (DNNs) are known to be
vulnerable to adversarial examples. Recent research has shown that checking the
intrinsic consistencies in the input data is a promising way to detect
adversarial attacks (e.g., by checking the object co-occurrence relationships
in complex scenes). However, existing approaches are tied to specific models
and do not offer generalizability. Motivated by the observation that language
descriptions of natural scene images have already captured the object
co-occurrence relationships that can be learned by a language model, we develop
a novel approach to perform context consistency checks using such language
models. The distinguishing aspect of our approach is that it is independent of
the deployed object detector and yet offers very high accuracy in terms of
detecting adversarial examples in practical scenes with multiple objects.
