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

2025/05/16 by Md Farhan Tasnim Oshim, Oshim, Md Farhan Tasnim, Nigel Doering +7 · 1 citation
Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Microwave Imaging and Scattering Analysis #Non-Invasive Vital Sign Monitoring #Wireless Body Area Networks

paper · pdf · doi:10.48550/arxiv.2505.10864

openalex publication_date 2025/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
• Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
• Physical-World Optical Adversarial Attacks on 3D Face Recognition
• IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing
• Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Square Attack: a query-efficient black-box adversarial attack via random\n search
• Simple Black-box Adversarial Attacks
• GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
• Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Towards Evaluating the Robustness of Neural Networks
• Adversarial examples in the physical world
• The Limitations of Deep Learning in Adversarial Settings
• DeepFool: a simple and accurate method to fool deep neural networks
• Explaining and Harnessing Adversarial Examples

Cited by

• PrivyWave: Privacy-Aware Wireless Sensing of Heartbeat

Related

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• PrivyWave: Privacy-Aware Wireless Sensing of Heartbeat
• Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion