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A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks. (arXiv:2209.14243v1 [cs.CR])
Sept. 29, 2022, 1:20 a.m. | Kevin Hector, Mathieu Dumont, Pierre-Alain Moellic, Jean-Max Dutertre
cs.CR updates on arXiv.org arxiv.org
Deep neural network models are massively deployed on a wide variety of
hardware platforms. This results in the appearance of new attack vectors that
significantly extend the standard attack surface, extensively studied by the
adversarial machine learning community. One of the first attack that aims at
drastically dropping the performance of a model, by targeting its parameters
(weights) stored in memory, is the Bit-Flip Attack (BFA). In this work, we
point out several evaluation challenges related to the BFA. First …
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