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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3112290
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
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|a Shahin Shamsabadi, Ali
|e verfasserin
|4 aut
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| 245 |
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|a Semantically Adversarial Learnable Filters
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 24.09.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We present an adversarial framework to craft perturbations that mislead classifiers by accounting for the image content and the semantics of the labels. The proposed framework combines a structure loss and a semantic adversarial loss in a multi-task objective function to train a fully convolutional neural network. The structure loss helps generate perturbations whose type and magnitude are defined by a target image processing filter. The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image from being classified with a label in the same group. We validate our framework with three different target filters, namely detail enhancement, log transformation and gamma correction filters; and evaluate the adversarially filtered images against three classifiers, ResNet50, ResNet18 and AlexNet, pre-trained on ImageNet. We show that the proposed framework generates filtered images with a high success rate, robustness, and transferability to unseen classifiers. We also discuss objective and subjective evaluations of the adversarial perturbations
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|a Journal Article
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|a Oh, Changjae
|e verfasserin
|4 aut
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| 700 |
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|a Cavallaro, Andrea
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 20., Seite 8075-8087
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|x 1941-0042
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|g volume:30
|g year:2021
|g day:20
|g pages:8075-8087
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|u http://dx.doi.org/10.1109/TIP.2021.3112290
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