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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 {\em misalignment} between the (human-specified) notion of robustness and the inherent geometry of the data.
Author Information
Andrew Ilyas (MIT)
Shibani Santurkar (MIT)
Dimitris Tsipras (MIT)
Logan Engstrom (MIT)
Brandon Tran (Massachusetts Institute of Technology)
Aleksander Madry (MIT)
Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.
Related Events (a corresponding poster, oral, or spotlight)
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2019 Spotlight: Adversarial Examples Are Not Bugs, They Are Features »
Tue Dec 10th 06:30 -- 06:35 PM Room West Exhibition Hall A
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2020 Poster: On Adaptive Attacks to Adversarial Example Defenses »
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2018 Tutorial: Adversarial Robustness: Theory and Practice »
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