  After a survey for person-tracking system-induced privacy concerns, we
propose a black-box adversarial attack method on state-of-the-art human
detection models called InvisibiliTee. The method learns printable adversarial
patterns for T-shirts that cloak wearers in the physical world in front of
person-tracking systems. We design an angle-agnostic learning scheme which
utilizes segmentation of the fashion dataset and a geometric warping process so
the adversarial patterns generated are effective in fooling person detectors
from all camera angles and for unseen black-box detection models. Empirical
results in both digital and physical environments show that with the
InvisibiliTee on, person-tracking systems' ability to detect the wearer drops
significantly.
