43 citations · 54 across the 8 of their papers we have counts for
8 papers
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Bao Gia Doan, Dang Quang Nguyen, Callum Lindquist +7
Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors possess unique characteristics, architecturally and in task execution; often operating in…
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…
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness
Bao Gia Doan, Shuiqiao Yang, Paul Montague +6
We present a new algorithm to train a robust malware detector. Modern malware detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations…
Transferable Graph Backdoor Attack
Shuiqiao Yang, Bao Gia Doan, Paul Montague +5
Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node featu…
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks
He Zhao, Thanh Nguyen, Trung Le +4
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many advers…
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
He Zhao, Trung Le, Paul Montague +3
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Recently, v…
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence
Yi Han, David Hubczenko, Paul Montague +6
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supe…
Reinforcement Learning for Autonomous Defence in Software-Defined Networking
Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham +6
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by…