Adversarial Framing for Image and Video Classification

Michał Zając,

Konrad Żołna,

Negar Rostamzadeh,

Pedro O. Pinheiro


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.

Słowa kluczowe: adversarial samples, convolutional neural networks, classification