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 .
@article{arxiv.1812.04599,
title = {Adversarial Framing for Image and Video Classification},
author = {Konrad Zolna and Michal Zajac and Negar Rostamzadeh and Pedro O. Pinheiro},
journal= {arXiv preprint arXiv:1812.04599},
year = {2019}
}
Comments
This is an extended version of the paper published at 33rd AAAI Conference on Artificial Intelligence (see https://doi.org/10.1609/aaai.v33i01.330110077 )