Drones' Cryptanalysis - Smashing Cryptography with a Flicker

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.