78 citations · 354 across the 35 of their papers we have counts for
37 papers
Towards quantum enhanced adversarial robustness in machine learning
Maxwell T. West, Shu-Lok Tsang, Jia S. Low +5
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input…
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals
Amila Silva, Ling Luo, Shanika Karunasekera +1
The emergence of social media as one of the main platforms for people to access news has enabled the wide dissemination of fake news. This has motivated numerous studies on automat…
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks
Marc Katzef, Andrew C. Cullen, Tansu Alpcan +2
The analysis of distributed techniques is often focused upon their efficiency, without considering their robustness (or lack thereof). Such a consideration is particularly importan…
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models
Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Protecting personal data against exploitation of machine learning models is crucial. Recently, availability attacks have shown great promise to provide an extra layer of protection…
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Maxwell T. West, Sarah M. Erfani, Christopher Leckie +3
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophisticati…