Work-in-Progress: Towards Evaluating CNNs Against Integrity Attacks on Multi-tenant Computation
2023 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)(2023)
摘要
We present an infrastructure for evaluating CNN models for vulnerability against a variety of integrity attacks. Our focus is on attacks that corrupt CNN computations with an impact on prediction/classification accuracy. The attack model encompasses a variety of mechanisms including injection of faults and glitches, integrity attacks on compute resources, etc. Our tool enables users to explore a variety of attack configurations, targets, and accuracy drops tolerated by the model. Experiments with our tool on publicly available CNN models show the vulnerability between layers is different, which can be exploited to protect important parts of the computation even when deployed on untrusted accelerators.
更多查看译文
关键词
Integrity Attacks,Multi-tenant,Neural Networks,Evaluation Tool,CNN
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要