The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models

ArXi:2602.16309v2 Announce Type: replace-cross Fault injection attacks on embedded neural network models have been shown as a potent threat. Numerous works studied resilience of models from various points of view. As of now, there is no comprehensive study that would evaluate the influence of number representations used for model parameters against electromagnetic fault injection (EMFI) attacks. In this paper, we investigate how four different number representations influence the success of an EMFI attack on embedded neural network models.