153 citations · 322 across the 21 of their papers we have counts for
8 papers · 1 filter
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Better Pay Attention Whilst Fuzzing
Shunkai Zhu, Jingyi Wang, Jun Sun +4
Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverag…
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Influence of spin fluctuations on structural phase transitions of iron
Ning Wang, Thomas Hammerschmidt, Tilmann Hickel +2
The effect of spin fluctuations on the (bcc) - (fcc) - (bcc) structural phase transitions in iron is investigated with a tight-binding (TB) model. The orthogonal -va…
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…
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…