Adversarial Robustness is at Odds with Lazy Training

Yunjuan Wang; Enayat Ullah; Poorya Mianjy; Raman Arora

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arXiv · 2207.00411

Adversarial Robustness is at Odds with Lazy Training

Yunjuan Wang·Enayat Ullah·Poorya Mianjy·Raman Arora

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Abstract

Recent works show that adversarial examples exist for random neural networks [Daniely and Schacham, 2020] and that these examples can be found using a single step of gradient ascent [Bubeck et al., 2021]. In this work, we extend this line of work to "lazy training" of neural networks -- a dominant model in deep learning theory in which neural networks are provably efficiently learnable. We show that over-parametrized neural networks that are guaranteed to generalize well and enjoy strong computational guarantees remain vulnerable to attacks generated using a single step of gradient ascent.

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Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora. 2022-06-18. Adversarial Robustness is at Odds with Lazy Training. https://arxiv.org/abs/2207.00411

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