Youcheng Sun
Queen's University Belfast
70 Papers
232 Citations
Youcheng Sun is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 16, co-authored 60 publications. Previous affiliations of Youcheng Sun include University of Oxford & Sant'Anna School of Advanced Studies.
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Papers
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang,Daniel Kroening,Wenjie Ruan,James Sharp,Youcheng Sun,Emese Thamo,Min Wu,Xinping Yi +7 more
TL;DR: A review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability as discussed by the authors.
390
Concolic testing for deep neural networks
Youcheng Sun,Min Wu,Wenjie Ruan,Xiaowei Huang,Marta Kwiatkowska,Daniel Kroening +5 more
- 03 Sep 2018
TL;DR: The first concolic testing approach for Deep Neural Networks (DNNs) is presented, which formalise coverage criteria for DNNs that have been studied in the literature, and develops a coherent method for performing concolicTesting to increase test coverage.
•Posted Content
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang,Daniel Kroening,Wenjie Ruan,James Sharp,Youcheng Sun,Emese Thamo,Min Wu,Xinping Yi +7 more
TL;DR: A review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability.
318
•Posted Content
Concolic Testing for Deep Neural Networks.
TL;DR: In this article, the authors present the first concolic testing approach for deep neural networks (DNNs), which combines program execution and symbolic analysis to explore the execution paths of a software program.
292
•Posted Content
Testing Deep Neural Networks
TL;DR: This paper proposes a family of four novel test criteria that are tailored to structural features of DNNs and their semantics, and validated by demonstrating that the generated test inputs guided via the proposed coverage criteria are able to capture undesired behaviours in a DNN.