  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 enables the reverse
engineering by changing the sign of intermediate values. We develop the first
exact extraction method on deep-layer feature extractor networks that provably
allows the recovery of the model parameters. Our experiments with Keras library
show that the precision error for the parameter recovery for the tested
networks is less than $10^{-13}$ with the usage of 64-bit floats, which
improves the current state of the art by 6 orders of magnitude. Additionally,
we discuss the protection techniques against fault injection attacks that can
be applied to enhance the fault resistance.
