SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition. (arXiv:2204.01560v2 [cs.CR] UPDATED)

Deep neural networks have empowered accurate device-free human activity
recognition, which has wide applications. Deep models can extract robust
features from various sensors and generalize well even in challenging
situations such as data-insufficient cases. However, these systems could be
vulnerable to input perturbations, i.e. adversarial attacks. We empirically
demonstrate that both black-box Gaussian attacks and modern adversarial
white-box attacks can render their accuracies to plummet. In this paper, we
firstly point out that such phenomenon can bring severe safety hazards …

adversarial attack device free human recognition