Exploring the vulnerabilities and enhancing the adversarial robustness of deep neural networks
Deep learning, especially deep neural networks (DNNs), is at the heart of the current rise of artificial intelligence, and the major breakthroughs in the last few years have been made by DNNs. It has been demonstrated in recent works that DNNs are vulnerable to human-crafted adversarial examples, wh...
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格式: | Thesis-Doctor of Philosophy |
語言: | English |
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Nanyang Technological University
2022
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在線閱讀: | https://hdl.handle.net/10356/160963 |
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機構: | Nanyang Technological University |
語言: | English |