Characterizing Adversarial Examples Based on
Spatial Consistency Information for Semantic
Segmentation
Abstract. Deep Neural Networks (DNNs) have been widely applied in
various recognition tasks. However, recently DNNs have been shown to
be vulnerable against adversarial examples, which can mislead DNNs to
make arbitrary incorrect predictions. While adversarial examples are well
studied in classification tasks, other learning problems may have different properties. For instance, semantic segmentation requires additional
components such as dilated convolutions and multiscale processing. In
this paper, we aim to characterize adversarial examples based on spatial
context information in semantic segmentation. We observe that spatial
consistency information can be potentially leveraged to detect adversarial examples robustly even when a strong adaptive attacker has access
to the model and detection strategies. We also show that adversarial
examples based on attacks considered within the paper barely transfer
among models, even though transferability is common in classification.
Our observations shed new light on developing adversarial attacks and
defenses to better understand the vulnerabilities of DNNs.