Anti-Sensing: Defense against Unauthorized Radar-based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators

• URL: http://arxiv.org/abs/2505.10864v1
• Date: Fri, 16 May 2025 05:07:08 GMT
• Title: Anti-Sensing: Defense against Unauthorized Radar-based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators
• Authors: Md Farhan Tasnim Oshim, Nigel Doering, Bashima Islam, Tsui-Wei Weng, Tauhidur Rahman,
• Abstract summary: Radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns.<n>We present Anti-Sensing, a novel defense mechanism designed to prevent unauthorized radar-based sensing.
• Score: 9.637355012130442
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in Ultra-Wideband (UWB) radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns, particularly in human-robot interactions and autonomous systems that rely on radar for sensing human presence and physiological functions. In this paper, we present Anti-Sensing, a novel defense mechanism designed to prevent unauthorized radar-based sensing. Our approach introduces physically realizable perturbations, such as oscillatory motion from wearable devices, to disrupt radar sensing by mimicking natural cardiac motion, thereby misleading heart rate (HR) estimations. We develop a gradient-based algorithm to optimize the frequency and spatial amplitude of these oscillations for maximal disruption while ensuring physiological plausibility. Through both simulations and real-world experiments with radar data and neural network-based HR sensing models, we demonstrate the effectiveness of Anti-Sensing in significantly degrading model accuracy, offering a practical solution for privacy preservation.

Related papers

• Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition [5.955900146668931]
  Recent research has shown high accuracy in recognizing subjects or gender from radar gait patterns, raising privacy concerns. This study addresses these issues by investigating privacy vulnerabilities in radar-based Human Activity Recognition (HAR) systems. We propose a novel method for privacy preservation using Differential Privacy (DP) driven by attributions derived with Integrated Decision Gradient (IDG) algorithm.
  arXiv  Detail & Related papers  (2024-11-04T14:08:26Z)
• Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer [10.388561519507471]
  This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing. It provides initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition.
  arXiv  Detail & Related papers  (2024-07-01T08:43:27Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Radar-Based Recognition of Static Hand Gestures in American Sign Language [17.021656590925005]
  This study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. The simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data.
  arXiv  Detail & Related papers  (2024-02-20T08:19:30Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures [4.92674421365689]
  We propose MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler signatures. It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points. MDPose outperforms state-of-the-art RF-based pose estimation systems.
  arXiv  Detail & Related papers  (2022-01-11T21:46:28Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.