TY - JOUR
T1 - Clean-Label Attack on Face Authentication Systems Through Rolling Shutter Mechanism
AU - Wang, Yufei
AU - Li, Haoliang
AU - Zhang, Liepiao
AU - Hu, Yongjian
AU - Kot, Alex C.
PY - 2025
Y1 - 2025
N2 - We introduce a novel clean-label black-box face presentation attack on face authentication systems, i.e., face recognition and verification systems, under mild conditions. Different from other clean-label attacks which require inserting complicated or intensity patterns after the image-capturing phase, our designed pattern can be automatically inserted during the exposure by utilizing the rolling shutter mechanism and modulating environment LEDs in a specialized waveform. This method provides a potential way to conduct backdoor attacks in the physical domain. Additionally, we propose an optimization strategy based on evolutionary computing to optimize the parameters of the stripe patterns, enhancing the attack success rate. The experimental results on several face recognition models and face verification services provided by the leading technology companies demonstrate the effectiveness of our attack method. Our study reveals a new attack applicable in the physical world, highlighting significant security concerns for existing face recognition, verification, and face anti-spoofing techniques. © 2024 IEEE.
AB - We introduce a novel clean-label black-box face presentation attack on face authentication systems, i.e., face recognition and verification systems, under mild conditions. Different from other clean-label attacks which require inserting complicated or intensity patterns after the image-capturing phase, our designed pattern can be automatically inserted during the exposure by utilizing the rolling shutter mechanism and modulating environment LEDs in a specialized waveform. This method provides a potential way to conduct backdoor attacks in the physical domain. Additionally, we propose an optimization strategy based on evolutionary computing to optimize the parameters of the stripe patterns, enhancing the attack success rate. The experimental results on several face recognition models and face verification services provided by the leading technology companies demonstrate the effectiveness of our attack method. Our study reveals a new attack applicable in the physical world, highlighting significant security concerns for existing face recognition, verification, and face anti-spoofing techniques. © 2024 IEEE.
KW - Clean-label black-box attack
KW - evolutionary computing
KW - face recognition/verification
KW - rolling shutter
UR - http://www.scopus.com/inward/record.url?scp=85208798835&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85208798835&origin=recordpage
U2 - 10.1109/LSP.2024.3493804
DO - 10.1109/LSP.2024.3493804
M3 - RGC 21 - Publication in refereed journal
SN - 1070-9908
VL - 32
SP - 36
EP - 40
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
ER -