Adversarial Framing for Image and Video Classification


  • Michał Zajac Jagiellonian University
  • Konrad Zołna Jagiellonian University
  • Negar Rostamzadeh Element AI
  • Pedro O. Pinheiro Element AI



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-theart 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




How to Cite

Zajac, M., Zołna, K., Rostamzadeh, N., & Pinheiro, P. O. (2019). Adversarial Framing for Image and Video Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 10077-10078.



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