J. Breier

TVLA misses subtle side-channel leakage in neural networks, but a new statistical test closes the gap.

Floating-point precision in embedded neural networks makes them shockingly vulnerable: a single electromagnetic fault injection can almost completely degrade accuracy, while integer representations offer significantly better resilience.