SNIFF : reverse engineering of neural networks with fault attacks

• Main Authors: Breier, Jakub, Jap, Dirmanto, Hou, Xiaolu, Bhasin, Shivam, Liu, Yang
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: 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
• Online Access: https://hdl.handle.net/10356/155678
• Institution: Nanyang Technological University

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...