Detecting adversarial samples for deep neural networks through mutation testing
Deep Neural Networks (DNNs) are adept at many tasks, with the more well-known task of image recognition using a subset of DNNs called Convolutional Neural Networks (CNNs). However, they are prone to attacks called adversarial attacks. Adversarial attacks are malicious modifications made on input sam...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138719 |