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SNIFF: Reverse Engineering of Neural Networks with Fault Attacks. (arXiv:2002.11021v2 [cs.CR] UPDATED)
cs.CR updates on arXiv.org arxiv.org
Neural networks have been shown to be vulnerable against fault injection
attacks. These attacks change the physical behavior of the device during the
computation, resulting in a change of value that is currently being computed.
They can be realized by various fault injection techniques, ranging from
clock/voltage glitching to application of lasers to rowhammer. In this paper we
explore the possibility to reverse engineer neural networks with the usage of
fault attacks. SNIFF stands for sign bit flip fault, which …
application attacks change computation device engineer engineering injection injection attacks networks neural networks physical reverse reverse engineer reverse engineering rowhammer techniques value vulnerable