  Neural networks are prone to adversarial attacks. In general, such attacks
deteriorate the quality of the input by either slightly modifying most of its
pixels, or by occluding it with a patch. In this paper, we propose a method
that keeps the image unchanged and only adds an adversarial framing on the
border of the image. We show empirically that our method is able to
successfully attack state-of-the-art methods on both image and video
classification problems. Notably, the proposed method results in a universal
attack which is very fast at test time. Source code can be found at
https://github.com/zajaczajac/adv_framing .
