1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CR2024★ 1 cited
Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague +6
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is…
cs.LG2023
It's Simplex! Disaggregating Measures to Improve Certified Robustness
Andrew C. Cullen, Paul Montague, Shijie Liu +2
Certified robustness circumvents the fragility of defences against adversarial attacks, by endowing model predictions with guarantees of class invariance for attacks up to a calcul…
cs.LG2023
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Anh Bui, Trung Le, He Zhao +3
Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model'…