In this paper, we presented a system to generate adversar-ial patches for person detectors that can be printed out andused in the real-world. We did this by optimising an imageto minimise different probabilities related to the appearanceof a person in the output of the detector. In our experimentswe compared different approaches and found that minimis-ing object loss created the most effective patches.
From our real-world test with printed out patches we canalso see that our patches work quite well in hiding personsfrom object detectors, suggesting that security systems us-ing similar detectors might be vulnerable to this kind of at-tack.
We believe that, if we combine this technique with asophisticated clothing simulation, we can design a T-shirtprint that can make a person virtually invisible for automaticsurveillance cameras (using the YOLO detector).
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