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Poster
A single gradient step finds adversarial examples on random two-layers neural networks
Sebastien Bubeck · Yeshwanth Cherapanamjeri · Gauthier Gidel · Remi Tachet des Combes

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ None #None
in Poster Session 3 »

Daniely and Schacham recently showed that gradient descent finds adversarial examples on random undercomplete two-layers ReLU neural networks. The term “undercomplete” refers to the fact that their proof only holds when the number of neurons is a vanishing fraction of the ambient dimension. We extend their result to the overcomplete case, where the number of neurons is larger than the dimension (yet also subexponential in the dimension). In fact we prove that a single step of gradient descent suffices. We also show this result for any subexponential width random neural network with smooth activation function.

Keywords: Theory · Deep Learning · Optimization · Adversarial Robustness and Security

Author Information

Sebastien Bubeck (Microsoft Research)
Yeshwanth Cherapanamjeri (UC Berkeley)
Gauthier Gidel (Mila)
Remi Tachet des Combes (MSR Montreal)

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