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In an "open skies" era in which drones fly among us, a new question arises: how can we tell whether a passing drone is being used by its operator for a legitimate purpose (e.g., delivering pizza) or an illegitimate purpose (e.g., taking a peek at a person showering in his/her own house)? Over the years, many methods have been suggested to detect the presence of a drone in a specific location, however since populated areas are no longer off limits for drone flights, the previously suggested methods for detecting a privacy invasion attack are irrelevant. In this paper, we present a new method that can detect whether a specific POI (point of interest) is being video streamed by a drone. We show that applying a periodic physical stimulus on a target/victim being video streamed by a drone causes a watermark to be added to the encrypted video traffic that is sent from the drone to its operator and how this watermark can be detected using interception. Based on this method, we present an algorithm for detecting a privacy invasion attack. We analyze the performance of our algorithm using four commercial drones (DJI Mavic Air, Parrot Bebop 2, DJI Spark, and DJI Mavic Pro). We show how our method can be used to (1) determine whether a detected FPV (first-person view) channel is being used to video stream a POI by a drone, and (2) locate a spying drone in space; we also demonstrate how the physical stimulus can be applied covertly. In addition, we present a classification algorithm that differentiates FPV transmissions from other suspicious radio transmissions. We implement this algorithm in a new invasion attack detection system which we evaluate in two use cases (when the victim is inside his/her house and when the victim is being tracked by a drone while driving his/her car); our evaluation shows that a privacy invasion attack can be detected by our system in about 2-3 seconds.

Advanced driver assistance systems (ADASs) were developed to reduce the number of car accidents by issuing driver alert or controlling the vehicle. In this paper, we tested the robustness of Mobileye, a popular external ADAS. We injected spoofed traffic signs into Mobileye to assess the influence of environmental changes (e.g., changes in color, shape, projection speed, diameter and ambient light) on the outcome of an attack. To conduct this experiment in a realistic scenario, we used a drone to carry a portable projector which projected the spoofed traffic sign on a driving car. Our experiments show that it is possible to fool Mobileye so that it interprets the drone carried spoofed traffic sign as a real traffic sign.

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