54 citations · 55 across the 2 of their papers we have counts for
2 papers
cs.CR2022★ 54 cited
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +4
Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however,…
cs.LG2022★ 1 cited
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
Shucen Ma, Jianqi Shi, Yanhong Huang +2
The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach. However, the research on understanding ML models, such as Random…