485 citations · 1.5k across the 72 of their papers we have counts for
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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…
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
Shijie Liu, Andrew C. Cullen, Paul Montague +2
Poisoning attacks can disproportionately influence model behaviour by making small changes to the training corpus. While defences against specific poisoning attacks do exist, they…
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…
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…
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Andrew C. Cullen, Shijie Liu, Paul Montague +2
In guaranteeing the absence of adversarial examples in an instance's neighbourhood, certification mechanisms play an important role in demonstrating neural net robustness. In this…
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Shu Lok Tsang, Maxwell T. West, Sarah M. Erfani +1
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and ide…