AroundConnect

2017

Adversarial Examples for Semantic Image Segmentation

Fischer, Volker, Kumar, Mummadi Chaithanya, Metzen, Jan Hendrik et al.

Understand

Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations.

  • So far this phenomenon has mainly been studied in the context of whole-image classification.
  • In this contribution, we analyse how adversarial perturbations can affect the task of semantic segmentation.
  • We show how existing adversarial attackers can be transferred to this task and that it is possible to create imperceptible adversarial perturbations that lead a deep network to misclassify almost all pixels of a chosen class while leaving network prediction nearly unchanged outside this class.

Built on

  • Intriguing properties of neural networks

    Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014

    Earlier work this paper cites.

  • Explaining and Harnessing Adversarial Examples

    Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015

    Earlier work this paper cites.

  • Semantic image segmentation via deep parsing network

    Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015

    Earlier work this paper cites.

Similar

  • Fully convolutional networks for semantic segmentation

    Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015

    Cited alongside, same era.

  • The cityscapes dataset for semantic urban scene understanding

    Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016

    Cited alongside, same era.

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