The Defense of Adversarial Example with Conditional Generative Adversarial Networks
Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2020-01-01
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| Series: | Security and Communication Networks |
| Online Access: | http://dx.doi.org/10.1155/2020/3932584 |