Generating Adversarial Samples With Constrained Wasserstein Distance
In recent years, deep neural network (DNN) approaches prove to be useful in many machine learning tasks, including classification. However, small perturbations that are carefully crafted by attackers can lead to the misclassification of the images. Previous studies have shown that adversarial subspa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8845708/ |