Towards Hiding Adversarial Examples from Network Interpretation

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https://arxiv.org/abs/1812.02843

Permanent Link

http://hdl.handle.net/11603/14294

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Author/Creator ORCID

Date

2018-12-06

Type of Work

journal articles
Text

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Citation of Original Publication

Akshayvarun Subramanya, Vipin Pillai, Hamed Pirsiavash, Towards Hiding Adversarial Examples from Network Interpretation, Computer Science , Computer Vision and Pattern Recognition, 2018, https://arxiv.org/abs/1812.02843

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Subjects

Deep networks
standard network interpretation algorithms
decision making process

Abstract

Deep networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches can be highlighted using standard network interpretation algorithms, thus revealing the identity of the adversary. We show that it is possible to create adversarial patches which not only fool the prediction, but also change what we interpret regarding the cause of prediction. We show that our algorithms can empower adversarial patches, by hiding them from network interpretation tools. We believe our algorithms can facilitate developing more robust network interpretation tools that truly explain the network's underlying decision making process.