Toward Drone Privacy via Regulating Altitude and Payload Zupei Li, University of Massachusetts Lowell Chao Gao, University of Massachusetts Lowell Qinggang Yue, University of Massachusetts Lowell Xinwen Fu, University of Central Florida Hello, everyone! My name is XXXX from XXXX university. Today my topic is xxxxxx. This is Collaborative work with Prof. Junzhou Luo from Southeast University, Prof. Kui Wu from University of Victoria, Prof. Wei Yu from Towson University, and Prof. Xinwen Fu from UMASS Lowell,

Outline Introduction Regulation for drone privacy Evaluation Conclusion

Unmanned Aerial Vehicles (UAVs) Gartner: around 3 million personal and commercial drones were shipped in 2017. The number of mini drones being sold worldwide demonstrates their increasing popularity. Commercial Military DJI Consumer https://www.toptal.com/finance/market-research-analysts/drone-market http://thedronegirl.com/2018/09/18/dji-market-share/ Goldman Sachs 2016 report Skylogic Research 2018

Drone Privacy It is critical to perform a rigid study of privacy threats from these drones equipped with cameras. We are the first to systematically study drones revealing user inputs on mobile devices and how to prevent the privacy leaking

Outline Introduction Regulation for drone privacy Evaluation Conclusion

Drone Attack to Disclose Text Typed on Touch Screen Step 1: Capturing videos Step 2: Deriving the keyboard layout, e.g. vernier caliper Step 3: Tracking the device via DPM (Deformable Parts Model) Step 4: Aligning the video via planar homography Step 5: Tracking fingertip movement via DPM Step 6: Analyzing fingertip motion Step 7: Deriving the passcode candidates Step 8: Ranking the passcode candidates

Example of Aligned Video

Target Size v.s. Drone Height Pinhole camera model l: Object’s width f: Camera focal length h: Distance between camera optical center C and the target p: pixel size on camera sensor ni: is the number of pixels of the object in the image

Observations Given the object width l, its size l’ in the video is inversely proportional to ratio of the height (h) over focal length (f). f’ increases if the ratio decreases. If the drone flies higher (larger h), to keep the device the same size in the video, f must increase f is limited by the camera make and model.

Impact of Drone Tilting The Angular Field of View (AFOV) – Angle ACB d: width of the image sensor on the image plane Observations: as the drone’s height h increases, AFOV decreases. Harder to maintain the target within the FOV of the camera if the drone flies very high.

Drone Drifting Away Wind may drive the drone away from its target The target may drift away from the field of view (FOV) of the drone camera Assume we keep l’ for a good accuracy and get Ddrift Ddrift does not change with the height The reason is that we have to increase the focal length to have the required target size l’ in the video as the height increases.

Privacy through Regulation ni: is the number of pixels of the object in the image Must be large enough to achieve a specific accuracy Regulating the minimum distance h from the drone to the ground Regulating long lens f

Outline Introduction Regulation for drone privacy Evaluation Conclusion

Experiment Setup GoPro Hero 4 at a narrow FOV setting with a recording resolution of 1920×1080 Sony PJ275 recording at the resolution of 1920×1080 Since Sony PJ275 has an optical zoom lens Two common attack scenarios: Drones taking videos of a victim inputting passwords on an iPad 2 and retrieving passwords Drones taking videos of a crowd and perform common facial recognition algorithm

Distance v.s. Password Inputting Obtain resolution of the object at a given specific success accuracy Calculate the distance for a specific camera setting Success rate 100% 75% 65% GoPro 2.75m 5.23m 6.42m Sony PJ275 77.61m 147.47m 181.1m

Drone v.s. Facial Recognition Assume the best case for the adversary a person lies on the ground Success Rate 99% 70% GoPro 1.18m 9.49m Sony PJ275 33.43m 267.48m

Outline Introduction Regulation for drone privacy Evaluation Conclusion

Conclusion Systematically study how to regulate the camera capability and altitude so that computer vision algorithms will perform poorly and user privacy on the ground can be preserved. Theoretically analyze the relationship between factors Camera focal length, distance between the target object and camera, and the size of the target object in an image Evaluate two scenarios Password inference Facial recognition

Thank you! Xinwen Fu 19/15

Contributions Systematically address drone privacy through regulating a drone’s payload and altitude. Analyze the image formation process of the camera and theoretically study how to keep objects in an image taken by a drone camera small in terms of pixels. Present the factors that affect the capability of cameras carried by drones.