Designing adversarial signals against three deep learning and non-deep learning methods
The widespread adoption of machine learning, especially Deep Neural Networks (DNNs) in daily life, causes great concern about its security property. Szegedy et al.'s study showed that DNNs are vulnerable to adversarial examples, which are images with small, deliberately designed perturbations....
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/151718 |