Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel

Tao Ni; Xiaokuan Zhang; Qingchuan Zhao

doi:10.1145/3576915.3623153

Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

34 Citations (Scopus)

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.

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
DOIs	https://doi.org/10.1145/3576915.3623153
Publication status	Published - 2023
Event	30th ACM Conference on Computer and Communications Security (ACM CCS 2023) - Tivoli Congress Center, Copenhagen, Denmark Duration: 26 Nov 2023 → 30 Nov 2023 https://www.sigsac.org/ccs/CCS2023/index.html

Conference

Conference	30th ACM Conference on Computer and Communications Security (ACM CCS 2023)
Abbreviated title	CCS 2023
Place	Denmark
City	Copenhagen
Period	26/11/23 → 30/11/23
Internet address	https://www.sigsac.org/ccs/CCS2023/index.html

Funding

We sincerely thank all anonymous reviewers for their constructive feedback. This work was supported by CityU APRC grant 9610563, Hong Kong RGC (CityU 21219223, C1029-22G). Any opinions, findings, and conclusions in this paper are those of the authors and not necessarily of the supported organizations.

Research Keywords

Electromagnetic side channels
In-display fingerprint sensors
denoising diffusion model

RGC Funding Information

RGC-funded

Access Output

10.1145/3576915.3623153

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{3e82b5ab293e4cf8badc956edbe5372e,

title = "Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel",

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. {\textcopyright} 2023 ACM.",

keywords = "Electromagnetic side channels, In-display fingerprint sensors, denoising diffusion model",

author = "Tao Ni and Xiaokuan Zhang and Qingchuan Zhao",

year = "2023",

doi = "10.1145/3576915.3623153",

language = "English",

isbn = "979-8-4007-0050-7",

pages = "253--267",

booktitle = "CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security",

publisher = "Association for Computing Machinery",

address = "United States",

note = "30th ACM Conference on Computer and Communications Security (ACM CCS 2023), CCS 2023 ; Conference date: 26-11-2023 Through 30-11-2023",

url = "https://www.sigsac.org/ccs/CCS2023/index.html",

}

Ni, T, Zhang, X & Zhao, Q 2023, Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel. in CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, New York, NY, pp. 253-267, 30th ACM Conference on Computer and Communications Security (ACM CCS 2023), Copenhagen, Denmark, 26/11/23. https://doi.org/10.1145/3576915.3623153

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.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel

AU - Ni, Tao

AU - Zhang, Xiaokuan

AU - Zhao, Qingchuan

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Electromagnetic side channels

KW - In-display fingerprint sensors

KW - denoising diffusion model

UR - http://www.scopus.com/inward/record.url?scp=85179837685&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85179837685&origin=recordpage

U2 - 10.1145/3576915.3623153

DO - 10.1145/3576915.3623153

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-4007-0050-7

SP - 253

EP - 267

BT - CCS '23 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

PB - Association for Computing Machinery

CY - New York, NY

T2 - 30th ACM Conference on Computer and Communications Security (ACM CCS 2023)

Y2 - 26 November 2023 through 30 November 2023

ER -

Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel

Abstract

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

ECS: Contactless Side Channels on Mobile Wireless Charging: Exploration and Mitigation

CRF: Enabling Metadata-private and Accountable Networks at Scale

Cite this

Recovering Fingerprints from In-Display Fingerprint Sensors via Electromagnetic Side Channel

Abstract

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

ECS: Contactless Side Channels on Mobile Wireless Charging: Exploration and Mitigation

CRF: Enabling Metadata-private and Accountable Networks at Scale

Cite this