SNIFF : reverse engineering of neural networks with fault attacks

• Main Authors: Breier, Jakub, Jap, Dirmanto, Hou, Xiaolu, Bhasin, Shivam, Liu, Yang
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Library and information science::Cryptography; Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Computer science and engineering::Hardware::Performance and reliability; Deep Learning; Fault Attacks
• 在線閱讀: https://hdl.handle.net/10356/155678

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various techniques, ranging from clock/voltage g...