14 citations · 33 across the 18 of their papers we have counts for
12 papers · 1 filter
What Was That Again? Certified Robustness for Automatic Speech Recognition
Andrew C. Cullen, Neil G. Marchant, Jiani Xie +2
Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations. While this has been repeatedly demonstrated using reference datasets, d…
Halt Fast! Early Stopping for Certified Robustness
Andrew C. Cullen, Paul Montague, Benjamin I. P. Rubinstein
Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs.…
Semantic Robustness Certification for Vision-Language Models
Peiyu Yang, Paul Montague, Feng Liu +4
Vision-language models (VLMs) are now widely used in downstream tasks. However, real-world applications often expose VLMs to distribution shifts induced by semantic variation (e.g.…
Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks
Jiani Xie, Andrew C. Cullen, Paul Montague +1
Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherent…
On the Bayes Inconsistency of Disagreement Discrepancy Surrogates
Neil G. Marchant, Andrew C. Cullen, Feng Liu +1
Deep neural networks often fail when deployed in real-world contexts due to distribution shift, a critical barrier to building safe and reliable systems. An emerging approach to ad…
Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning
Shijie Liu, Andrew C. Cullen, Paul Montague +2
Similar to other machine learning frameworks, Offline Reinforcement Learning (RL) is shown to be vulnerable to poisoning attacks, due to its reliance on externally sourced datasets…
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
Shijie Liu, Andrew C. Cullen, Paul Montague +2
The current state-of-the-art backdoor attacks against Reinforcement Learning (RL) rely upon unrealistically permissive access models, that assume the attacker can read (or even wri…
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…
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…
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…
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…