Towards Hiding Adversarial Examples from Network Interpretation
Links to Fileshttps://arxiv.org/abs/1812.02843
MetadataShow full item record
Type of Work10 pages
conference papers and proceedings preprints
Citation of Original PublicationAkshayvarun Subramanya, Vipin Pillai, Hamed Pirsiavash, Towards Hiding Adversarial Examples from Network Interpretation, Computer Vision and Pattern Recognition , 2018, https://arxiv.org/abs/1812.02843
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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.