Hierarchical Distribution-Aware Testing of Deep Learning

1 code implementation • 17 May 2022 • Wei Huang, Xingyu Zhao, Alec Banks, Victoria Cox, Xiaowei Huang

In this paper, we propose a new robustness testing approach for detecting AEs that considers both the feature level distribution and the pixel level distribution, capturing the perceptual quality of adversarial perturbations.

Adversarial Robustness Data Compression

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

no code implementations • 30 Nov 2021 • Yi Dong, Wei Huang, Vibhav Bharti, Victoria Cox, Alec Banks, Sen Wang, Xingyu Zhao, Sven Schewe, Xiaowei Huang

The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety.

Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles

1 code implementation • 2 Jun 2021 • Xingyu Zhao, Wei Huang, Alec Banks, Victoria Cox, David Flynn, Sven Schewe, Xiaowei Huang

The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications.