SNIFF: Reverse Engineering of Neural Networks with Fault Attacks

Understand

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.