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.

通过使用语言模型，我们开发了一种新方法来检测图像义务的一致性，即在自然场景的语言描述中捕捉物体共现关系，这种方法不仅适用于特定模型，而且对于多个物体的实际场景中检测对抗性样本具有非常高的精度。