1. 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,

2. Outline Introduction Regulation for drone privacy Evaluation
Conclusion

3. 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
Goldman Sachs
2016 report
Skylogic Research
2018

4. 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

5. Outline Introduction Regulation for drone privacy Evaluation
Conclusion

6. 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

7. Example of Aligned Video

8. 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

9. 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.

10. 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.

11. 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.

12. 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

13. Outline Introduction Regulation for drone privacy Evaluation
Conclusion

14. 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

15. 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

16. 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

17. Outline Introduction Regulation for drone privacy Evaluation
Conclusion

18. 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

19. Thank you!
Xinwen Fu
19/15

20. 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.