Streamlining DNN obfuscation to defend against model stealing attacks
Side-channel-based Deep Neural Network (DNN) model stealing has become a major concern with the advent of learning-based attacks. In respond to this threat, defence mechanisms have been presented to obfuscate the DNN execution, making it difficult to infer the correlation between side-channel inform...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178547 https://ieee-cas.org/event/conference/2024-ieee-international-symposium-circuits-and-systems |