Investigating the causes of the vulnerability of CNNs to adversarial perturbations: learning objective, model components, and learned representations
This work focuses on understanding how adversarial perturbations can disrupt the behavior of Convolutional Neural Networks (CNNs). Here, it is hypothesized that some components may be more vulnerable than others, unlike other research that considers a model vulnerable as a whole. Identifying model-s...
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格式: | Thesis-Master by Research |
語言: | English |
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Nanyang Technological University
2023
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在線閱讀: | https://hdl.handle.net/10356/171336 |
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機構: | Nanyang Technological University |
語言: | English |