51 citations · 348 across the 61 of their papers we have counts for
66 papers
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Miguel Ortiz del Castillo, Jonathan Morgan, Jack McRobbie +12
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders o…
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…
A Primer on the Data Cleaning Pipeline
Rebecca C. Steorts
The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, amo…
IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks
Xuanli He, Jun Wang, Benjamin Rubinstein +1
Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the t…
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation
Xuanli He, Qiongkai Xu, Jun Wang +2
Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be imp…
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…
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Zhuoqun Huang, Neil G. Marchant, Keane Lucas +3
Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused…
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Andrew C. Cullen, Paul Montague, Shijie Liu +2
In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of…
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes
J. Hyam Rubinstein, Benjamin I. P. Rubinstein
The sample compressibility of concept classes plays an important role in learning theory, as a sufficient condition for PAC learnability, and more recently as an avenue for robust…
Testing the Robustness of Learned Index Structures
Matthias Bachfischer, Renata Borovica-Gajic, Benjamin I. P. Rubinstein
While early empirical evidence has supported the case for learned index structures as having favourable average-case performance, little is known about their worst-case performance…
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness
Jiankai Jin, Olga Ohrimenko, Benjamin I. P. Rubinstein
Adversarial examples pose a security risk as they can alter decisions of a machine learning classifier through slight input perturbations. Certified robustness has been proposed as…
State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing
Dongge Liu, Van-Thuan Pham, Gidon Ernst +2
The statefulness property of network protocol implementations poses a unique challenge for testing and verification techniques, including Fuzzing. Stateful fuzzers tackle this chal…
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems
Jiankai Jin, Eleanor McMurtry, Benjamin I. P. Rubinstein +1
Differential privacy is a de facto privacy framework that has seen adoption in practice via a number of mature software platforms. Implementation of differentially private (DP) mec…
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +2
The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. S…
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning
Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld
The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models…
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein +1
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significan…
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Jun Wang, Chang Xu, Francisco Guzman +3
Mistranslated numbers have the potential to cause serious effects, such as financial loss or medical misinformation. In this work we develop comprehensive assessments of the robust…
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning
Jun Wang, Chang Xu, Francisco Guzman +4
Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.…
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu +6
Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from diff…
Not fit for Purpose: A critical analysis of the 'Five Safes'
Chris Culnane, Benjamin I. P. Rubinstein, David Watts
Adopted by government agencies in Australia, New Zealand and the UK as policy instrument or as embodied into legislation, the 'Five Safes' framework aims to manage risks of releasi…
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning
Chang Xu, Jun Wang, Yuqing Tang +3
As modern neural machine translation (NMT) systems have been widely deployed, their security vulnerabilities require close scrutiny. Most recently, NMT systems have been found vuln…
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein +1
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significan…
A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy
Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang +1
Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has dri…