173 citations · 854 across the 53 of their papers we have counts for
6 papers · 1 filter
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Min Wu, Matthew Wicker, Wenjie Ruan +2
Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise r…
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska
Verifying correctness of deep neural networks (DNNs) is challenging. We study a generic reachability problem for feed-forward DNNs which, for a given set of inputs to the network a…
Concolic Testing for Deep Neural Networks
Youcheng Sun, Min Wu, Wenjie Ruan +3
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for…
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm
Wenjie Ruan, Min Wu, Youcheng Sun +3
Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adv…
Testing Deep Neural Networks
Youcheng Sun, Xiaowei Huang, Daniel Kroening +3
Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional so…