485 citations · 1.5k across the 72 of their papers we have counts for
7 papers · 1 filter
AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds
Qizhou Wang, Hanxun Huang, Guansong Pang +2
Speech synthesis systems can now produce highly realistic vocalisations that pose significant authenticity challenges. Despite substantial progress in deepfake detection models, th…
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…
Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios
Yixin Su, Wei Jiang, Fangquan Lin +6
In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jo…
Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimension
Canaan Yung, Hanxun Huang, Christopher Leckie +1
Adversarial prompts are capable of jailbreaking frontier large language models (LLMs) and inducing undesirable behaviours, posing a significant obstacle to their safe deployment. C…
HALO: Robust Out-of-Distribution Detection via Joint Optimisation
Hugo Lyons Keenan, Sarah Erfani, Christopher Leckie
Effective out-of-distribution (OOD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that OOD detecti…