Adversarial examples are not bugs, they are features
Name
NeurIPS-2019-adversarial-examples-are-not-bugs-they-are-features-Paper.pdf
Description
Published version
Size
1.49 MB
Format
Adobe PDF
Checksum (MD5)
2eb64a5126d34e888edc153b214aa91c
Author(s)
Ilyas, A
Santurkar, S
Tsipras, D
Engstrom, L
Tran, B
Madry, A
Date Issued
2019
Journal
Advances in Neural Information Processing Systems
Citation
Ilyas, A, Santurkar, S, Tsipras, D, Engstrom, L, Tran, B et al. 2019. "Adversarial examples are not bugs, they are features." Advances in Neural Information Processing Systems, 32.
Version
Final published version
Abstract
© 2019 Neural information processing systems foundation. All rights reserved. Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features (derived from patterns in the data distribution) that are highly predictive, yet brittle and (thus) incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2019/hash/e2c420d928d4bf8ce0ff2ec19b371514-Abstract.html
