  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 are
vulnerable to adversarial attacks. However, the current two categories of
adversarial attacks, namely digital attacks and physical attacks both have
drawbacks, with the former ones impractical and the latter one conspicuous,
high-computational and inexecutable. To address the issues, we propose a
practical, executable, inconspicuous and low computational adversarial attack
based on LED illumination modulation. To fool the systems, the proposed attack
generates imperceptible luminance changes to human eyes through fast intensity
modulation of scene LED illumination and uses the rolling shutter effect of
CMOS image sensors in face recognition systems to implant luminance information
perturbation to the captured face images. In summary,we present a
denial-of-service (DoS) attack for face detection and a dodging attack for face
verification. We also evaluate their effectiveness against well-known face
detection models, Dlib, MTCNN and RetinaFace , and face verification models,
Dlib, FaceNet,and ArcFace.The extensive experiments show that the success rates
of DoS attacks against face detection models reach 97.67%, 100%, and 100%,
respectively, and the success rates of dodging attacks against all face
verification models reach 100%.

Computer Vision and Pattern Recognition;Artificial Intelligence