335 citations · 415 across the 8 of their papers we have counts for
5 papers · 1 filter
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
Probabilistic Verification of Neural Networks Against Group Fairness
Bing Sun, Jun Sun, Ting Dai +1
Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neu…
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
Yifan Jia, Jingyi Wang, Christopher M. Poskitt +3
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors b…
Attack as Defense: Characterizing Adversarial Examples using Robustness
Zhe Zhao, Guangke Chen, Jingyi Wang +3
As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to…
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Jingyi Wang, Jialuo Chen, Youcheng Sun +4
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction…