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Although face recognition starts to play an important role in our daily life,we need to pay attention that data-driven face recognition vision systems arevulnerable to adversarial attacks. However, the current two categories ofadversarial attacks, namely digital attacks and physical attacks both havedrawbacks, with the former ones impractical and the latter one conspicuous,high-computational and inexecutable. To address the issues, we propose apractical, executable, inconspicuous and low computational adversarial attackbased on LED illumination modulation. To fool the systems, the proposed attackgenerates imperceptible luminance changes to human eyes through fast intensitymodulation of scene LED illumination and uses the rolling shutter effect ofCMOS image sensors in face recognition systems to implant luminance informationperturbation to the captured face images. In summary,we present adenial-of-service (DoS) attack for face detection and a dodging attack for faceverification. We also evaluate their effectiveness against well-known facedetection models, Dlib, MTCNN and RetinaFace , and face verification models,Dlib, FaceNet,and ArcFace.The extensive experiments show that the success ratesof DoS attacks against face detection models reach 97.67%, 100%, and 100%,respectively, and the success rates of dodging attacks against all faceverification models reach 100%.

Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation