A hardware neural network backdoor that hides in plain sight

Deep learning systems on phones, cars, and other edge devices increasingly run on custom silicon. Specialized chips such as FPGAs and ASICs give these systems the speed and low power consumption that edge applications need. Many of these chips come from third-party design houses and foundries, which adds steps to the supply chain where an outside party can alter a device. Researchers at the University of Tennessee and the University of Florida built an attack … appeared first on Help Net Security.