Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security |
| Place of Publication | New York, NY |
| Publisher | Association for Computing Machinery |
| Pages | 253-267 |
| Number of pages | 15 |
| ISBN (print) | 979-8-4007-0050-7 |
| Publication status | Published - 2023 |
Conference
| Title | 30th ACM Conference on Computer and Communications Security (ACM CCS 2023) |
|---|---|
| Location | Tivoli Congress Center |
| Place | Denmark |
| City | Copenhagen |
| Period | 26 - 30 November 2023 |
Link(s)
Abstract
Recently, in-display fingerprint sensors have been widely adopted in newly-released smartphones. However, we find this new technique can leak information about the user's fingerprints during a screen-unlocking process via the electromagnetic (EM) side channel that can be exploited for fingerprint recovery. We propose FPLogger to demonstrate the feasibility of this novel side-channel attack. Specifically, it leverages the emitted EM emanations when the user presses the in-display fingerprint sensor to extract fingerprint information, then maps the captured EM signals to fingerprint images and develops 3D fingerprint pieces to spoof and unlock the smartphones. We have extensively evaluated the effectiveness of FPlogger on five commodity smartphones equipped with both optical and ultrasonic in-display fingerprint sensors, and the results show it achieves promising similarities in recovering fingerprint images. In addition, results from 50 end-to-end spoofing attacks also present FPLogger achieves 24% (top-1) and 54% (top-3) success rates in spoofing five different smartphones. © 2023 ACM.
Research Area(s)
- Electromagnetic side channels, In-display fingerprint sensors, denoising diffusion model
Citation Format(s)
Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel. / Ni, Tao; Zhang, Xiaokuan; Zhao, Qingchuan.
CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. New York, NY: Association for Computing Machinery, 2023. p. 253-267.
CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. New York, NY: Association for Computing Machinery, 2023. p. 253-267.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
