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SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition
[article]
2022
arXiv
pre-print
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
arXiv:2204.01560v2
fatcat:txd6iqyxcnbz3kfqlcmys45d6e