  Human can easily recognize visual objects with lost information: even losing
most details with only contour reserved, e.g. cartoon. However, in terms of
visual perception of Deep Neural Networks (DNNs), the ability for recognizing
abstract objects (visual objects with lost information) is still a challenge.
In this work, we investigate this issue from an adversarial viewpoint: will the
performance of DNNs decrease even for the images only losing a little
information? Towards this end, we propose a novel adversarial attack, named
\textit{AdvDrop}, which crafts adversarial examples by dropping existing
information of images. Previously, most adversarial attacks add extra
disturbing information on clean images explicitly. Opposite to previous works,
our proposed work explores the adversarial robustness of DNN models in a novel
perspective by dropping imperceptible details to craft adversarial examples. We
demonstrate the effectiveness of \textit{AdvDrop} by extensive experiments, and
show that this new type of adversarial examples is more difficult to be
defended by current defense systems.

Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning;Image and Video Processing