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

arXiv: https://arxiv.org/abs/2211.05854 · HTML: https://mlsanity.com/paper/2211.05854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://mlsanity.com/paper/2010.03593/) (2020)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [A Self-supervised Approach for Adversarial Robustness](https://mlsanity.com/paper/2006.04924/) (2020)
- [Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks](https://mlsanity.com/paper/2110.12976/) (2021)
- [Evaluating the Adversarial Robustness of Adaptive Test-time Defenses](https://mlsanity.com/paper/2202.13711/) (2022)
- [Adversarial Attacks are Reversible with Natural Supervision](https://mlsanity.com/paper/2103.14222/) (2021)
- [Attacking Adversarial Attacks as A Defense](https://mlsanity.com/paper/2106.04938/) (2021)
- [Combating Adversaries with Anti-Adversaries](https://mlsanity.com/paper/2103.14347/) (2021)
- [Towards Robust Neural Networks via Close-loop Control](https://mlsanity.com/paper/2102.01862/) (2021)
- [Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks](https://mlsanity.com/paper/2105.08714/) (2021)
- [AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense](https://mlsanity.com/paper/2107.06456/) (2021)
- [Ghost Peak: Practical Distance Reduction Attacks Against HRP UWB Ranging](https://mlsanity.com/paper/2111.05313/) (2021)
- [Evil Never Sleeps: When Wireless Malware Stays On After Turning Off iPhones](https://mlsanity.com/paper/2205.06114/) (2022)

## Built on by (most cited first, 1 of 1)

- [Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems](https://mlsanity.com/paper/2401.08863/) (2024)

## Abstract (arXiv)

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).