Vulnerability analysis on noise-injection based hardware attack on deep neural networks
Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to adversarial examples. Recent studies show that adding carefully crafted small perturbations on input layer can mislead a classifier into arbitrary categories. However, most adversarial attack algorith...
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Main Authors: | , , |
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2020
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Online Access: | https://hdl.handle.net/10356/136863 |
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Institution: | Nanyang Technological University |
Language: | English |
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