Clean-Label Attack on Face Authentication Systems Through Rolling Shutter Mechanism

Yufei Wang, Haoliang Li, Liepiao Zhang, Yongjian Hu, Alex C. Kot

Abstract: 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.

External IDs:doi:10.1109/lsp.2024.3493804