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Attacking Adversarial Defences by Smoothing the Loss Landscape. (arXiv:2208.00862v2 [cs.LG] UPDATED)
Aug. 8, 2022, 1:20 a.m. | Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
cs.CR updates on arXiv.org arxiv.org
This paper investigates a family of methods for defending against adversarial
attacks that owe part of their success to creating a noisy, discontinuous, or
otherwise rugged loss landscape that adversaries find difficult to navigate. A
common, but not universal, way to achieve this effect is via the use of
stochastic neural networks. We show that this is a form of gradient
obfuscation, and propose a general extension to gradient-based adversaries
based on the Weierstrass transform, which smooths the surface of …
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