Verification of bit-flip attacks against quantized neural networks

• Main Authors: ZHANG, Yedi, HUANG, Lei, GAO, Pengfei, SONG, Fu, SUN, Jun, DONG, Jin Song
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: Bit-Flip Attacks; Formal Verification; Quantized Neural Networks; Robustness; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/10159; https://ink.library.smu.edu.sg/context/sis_research/article/11159/viewcontent/Bit_FlipAttacks_av.pdf
• Institution: Singapore Management University

In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amount of bits within its parameter storage memory system to induce harmful behavior), has emerged as a relevant area of re...