Apr 19, 2017 � This work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations.
In this paper, we investigate the ef- fect of adversarial attacks on tasks involving a localization component, more specifically: semantic image segmenta- tion.
While deep learning is remarkably successful on percep- tual tasks, it was also shown to be vulnerable to adversar- ial perturbations of the input.
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This work presents an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output.
This work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations.
The first category consists of common attacks that were directly transferred from image classification to semantic segmentation models, making use of the�...
Apr 19, 2017 � While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach�...
This paper presents what to their knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models.
Oct 26, 2016 � We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such�...
We propose a Universal Dense Object Suppression (U-DOS) algorithm to derive the universal adversarial perturbations against object detection.