Real-World Adversarial Attacks on RF-Based Drone Detectors
This addresses security vulnerabilities in drone detection systems for real-world applications, representing a novel physical attack rather than an incremental digital one.
The paper tackled the problem of physical adversarial attacks on RF-based drone detectors by optimizing class-specific universal complex baseband perturbation waveforms transmitted alongside legitimate communications, resulting in reliable reduction of target drone detection while preserving detection of legitimate drones in over-the-air experiments with four drone types.
Radio frequency (RF) based systems are increasingly used to detect drones by analyzing their RF signal patterns, converting them into spectrogram images which are processed by object detection models. Existing RF attacks against image based models alter digital features, making over-the-air (OTA) implementation difficult due to the challenge of converting digital perturbations to transmittable waveforms that may introduce synchronization errors and interference, and encounter hardware limitations. We present the first physical attack on RF image based drone detectors, optimizing class-specific universal complex baseband (I/Q) perturbation waveforms that are transmitted alongside legitimate communications. We evaluated the attack using RF recordings and OTA experiments with four types of drones. Our results show that modest, structured I/Q perturbations are compatible with standard RF chains and reliably reduce target drone detection while preserving detection of legitimate drones.