Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018-10-06

AUTHORS

Chaowei Xiao , Ruizhi Deng , Bo Li , Fisher Yu , Mingyan Liu , Dawn Song

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. More... »

PAGES

220-237

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-01249-6_14

DOI

http://dx.doi.org/10.1007/978-3-030-01249-6_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107454674


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