  While machine learning applications are getting mainstream owing to a
demonstrated efficiency in solving complex problems, they suffer from inherent
vulnerability to adversarial attacks. Adversarial attacks consist of additive
noise to an input which can fool a detector. Recently, successful real-world
printable adversarial patches were proven efficient against state-of-the-art
neural networks. In the transition from digital noise based attacks to
real-world physical attacks, the myriad of factors affecting object detection
will also affect adversarial patches. Among these factors, view angle is one of
the most influential, yet under-explored. In this paper, we study the effect of
view angle on the effectiveness of an adversarial patch. To this aim, we
propose the first approach that considers a multi-view context by combining
existing adversarial patches with a perspective geometric transformation in
order to simulate the effect of view angle changes. Our approach has been
evaluated on two datasets: the first dataset which contains most real world
constraints of a multi-view context, and the second dataset which empirically
isolates the effect of view angle. The experiments show that view angle
significantly affects the performance of adversarial patches, where in some
cases the patch loses most of its effectiveness. We believe that these results
motivate taking into account the effect of view angles in future adversarial
attacks, and open up new opportunities for adversarial defenses.
