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Konrad Zołna

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2 papers

1 author row

AAAI Conference 2019 Short Paper

Adversarial Framing for Image and Video Classification

Michał Zajac
Konrad Zołna
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-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 github. com/zajaczajac/adv_framing.

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AAAI Conference 2019 Short Paper

Classifier-Agnostic Saliency Map Extraction

Konrad Zołna
Krzysztof J. Geras
Kyunghyun Cho

Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction. This allows to find all parts of the image that any classifier could use, not just one given in advance. This way we extract much higher quality saliency maps.

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