  Over the past decade, deep learning (DL) has been successfully applied to
many industrial domain-specific tasks. However, the current state-of-the-art DL
software still suffers from quality issues, which raises great concern
especially in the context of safety- and security-critical scenarios.
Adversarial examples (AEs) represent a typical and important type of defects
needed to be urgently addressed, on which a DL software makes incorrect
decisions. Such defects occur through either intentional attack or
physical-world noise perceived by input sensors, potentially hindering further
industry deployment. The intrinsic uncertainty nature of deep learning
decisions can be a fundamental reason for its incorrect behavior. Although some
testing, adversarial attack and defense techniques have been recently proposed,
it still lacks a systematic study to uncover the relationship between AEs and
DL uncertainty. In this paper, we conduct a large-scale study towards bridging
this gap. We first investigate the capability of multiple uncertainty metrics
in differentiating benign examples (BEs) and AEs, which enables to characterize
the uncertainty patterns of input data. Then, we identify and categorize the
uncertainty patterns of BEs and AEs, and find that while BEs and AEs generated
by existing methods do follow common uncertainty patterns, some other
uncertainty patterns are largely missed. Based on this, we propose an automated
testing technique to generate multiple types of uncommon AEs and BEs that are
largely missed by existing techniques. Our further evaluation reveals that the
uncommon data generated by our method is hard to be defended by the existing
defense techniques with the average defense success rate reduced by 35\%. Our
results call for attention and necessity to generate more diverse data for
evaluating quality assurance solutions of DL software.
