Adversarial camera stickers: A physical camera-based attack on deep learning systemsAdversarial camera stickers: A physical camera-based attack on deep learning systemsLi, Juncheng and Schmidt, Frank R. and Kolter, J. Zico2019
Paper summarydavidstutzLi et al. propose camera stickers that when computed adversarially and physically attached to the camera leads to mis-classification. As illustrated in Figure 1, these stickers are realized using circular patches of uniform color. These individual circular stickers are computed in a gradient-descent fashion by optimizing their location, color and radius. The influence of the camera on these stickers is modeled realistically in order to guarantee success.
https://i.imgur.com/xHrqCNy.jpg
Figure 1: Illustration of adversarial stickers on the camera (left) and the effect on the taken photo (right).
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Li, Juncheng
and
Schmidt, Frank R.
and
Kolter, J. Zico
arXiv e-Print archive - 2019 via Local Bibsonomy
Keywords:
dblp
Li et al. propose camera stickers that when computed adversarially and physically attached to the camera leads to mis-classification. As illustrated in Figure 1, these stickers are realized using circular patches of uniform color. These individual circular stickers are computed in a gradient-descent fashion by optimizing their location, color and radius. The influence of the camera on these stickers is modeled realistically in order to guarantee success.
https://i.imgur.com/xHrqCNy.jpg
Figure 1: Illustration of adversarial stickers on the camera (left) and the effect on the taken photo (right).
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).