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Adversarial Examples for Semantic Image Segmentation
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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.
Forward citations
Cited by 2 Pith papers
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On the Robustness of Human Pose Estimation
Human pose estimation models are relatively robust to single-step attacks, but heatmap-based and structure-aware models resist attacks better than direct-regression models, and universal perturbations can still break them.
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Universal, transferable and targeted adversarial attacks
A trained encoder-decoder network (FTN) transforms source images into targeted adversarial examples that reportedly transfer across VGG19, Inception-v3, ResNet variants, DenseNet, and a black-box commercial classifier...
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