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arXiv · 2211.05854

Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses

Abhiram Kolli·Muhammad Jehanzeb Mirza·Horst Possegger·Horst Bischof
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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).

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Abhiram Kolli, Muhammad Jehanzeb Mirza, Horst Possegger, Horst Bischof. 2022-11-10. Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses. https://arxiv.org/abs/2211.05854

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