  Deep Neural Networks (DNNs) have achieved excellent performance in various
fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their
deployments to safety-critical applications. This paper presents a novel AE
detection framework, named BEYOND, for trustworthy predictions. BEYOND performs
the detection by distinguishing the AE's abnormal relation with its augmented
versions, i.e. neighbors, from two prospects: representation similarity and
label consistency. An off-the-shelf Self-Supervised Learning (SSL) model is
used to extract the representation and predict the label for its highly
informative representation capacity compared to supervised learning models. For
clean samples, their representations and predictions are closely consistent
with their neighbors, whereas those of AEs differ greatly. Furthermore, we
explain this observation and show that by leveraging this discrepancy BEYOND
can effectively detect AEs. We develop a rigorous justification for the
effectiveness of BEYOND. Furthermore, as a plug-and-play model, BEYOND can
easily cooperate with the Adversarial Trained Classifier (ATC), achieving the
state-of-the-art (SOTA) robustness accuracy. Experimental results show that
BEYOND outperforms baselines by a large margin, especially under adaptive
attacks. Empowered by the robust relation net built on SSL, we found that
BEYOND outperforms baselines in terms of both detection ability and speed. Our
code will be publicly available.
