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- W4309043648 abstract "Keyless entry systems in cars are adopting neural networks for localizing its operators. Using test-time adversarial defences equip such systems with the ability to defend against adversarial attacks without prior training on adversarial samples. We propose a test-time adversarial example detector which detects the input adversarial example through quantifying the localized intermediate responses of a pre-trained neural network and confidence scores of an auxiliary softmax layer. Furthermore, in order to make the network robust, we extenuate the non-relevant features by non-iterative input sample clipping. Using our approach, mean performance over 15 levels of adversarial perturbations is increased by 55.33% for the fast gradient sign method (FGSM) and 6.3% for both the basic iterative method (BIM) and the projected gradient method (PGD)." @default.
- W4309043648 created "2022-11-21" @default.
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- W4309043648 date "2022-11-10" @default.
- W4309043648 modified "2023-10-14" @default.
- W4309043648 title "Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses" @default.
- W4309043648 doi "https://doi.org/10.48550/arxiv.2211.05854" @default.
- W4309043648 hasPublicationYear "2022" @default.
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