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Poster
Exploring the Landscape of Spatial Robustness
Logan Engstrom · Brandon Tran · Dimitris Tsipras · Ludwig Schmidt · Aleksander Madry
The study of adversarial robustness has so far largely focused on perturbations
bound in $\ell_p$-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of
neural network--based classifiers to rotations and translations. While data
augmentation offers relatively small robustness, we use ideas from robust
optimization and test-time input aggregation to significantly improve robustness.
Finally we find that, in contrast to the $\ell_p$-norm case, first-order
methods cannot reliably find worst-case perturbations. This highlights
spatial robustness as a fundamentally different setting requiring additional
study.
Author Information
Logan Engstrom (MIT)
Brandon Tran (MIT)
Dimitris Tsipras (MIT)
Ludwig Schmidt (UC Berkeley)
Aleksander Madry (MIT)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Exploring the Landscape of Spatial Robustness »
Wed Jun 12th 11:20 -- 11:25 PM Room Seaside Ballroom
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