Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical
scenarios, thus the analysis of robustness for c-MARL models is profoundly important …

Previous studies have shown that universal adversarial attacks can fool deep neural
networks over a large set of input images with a single human-invisible perturbation …

Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric
transformation. This paper aims to verify the robustness of large-scale DNNs against the …

Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it
can be weakened by adversarial attacks. Recent studies have introduced``smoothed …

The vulnerability of deep neural networks (DNNs) to input perturbations has posed a
significant challenge. Recent work on robustness verification of DNNs not only lacks …

Adversarial training is extensively utilized to improve the adversarial robustness of deep
neural networks. Yet mitigating the degradation of standard generalization performance in …

Formal methods and machine learning are two research fields with drastically different
foundations and philosophies. Formal methods utilise mathematically rigorous techniques …

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its
algorithmic robustness against adversarial perturbations remains unexplored. The attacks …

When textual classifiers are deployed in safety-critical workflows, they must withstand the
onslaught of AI-enabled model confusion caused by adversarial examples with minor …