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Xiaofei Xie
Researcher at Nanyang Technological University
Publications - 143
Citations - 3102
Xiaofei Xie is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Fuzz testing. The author has an hindex of 22, co-authored 107 publications receiving 1555 citations. Previous affiliations of Xiaofei Xie include Tianjin University & Kyushu University.
Papers
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Proceedings ArticleDOI
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
Xiaofei Xie,Lei Ma,Felix Juefei-Xu,Minhui Xue,Hongxu Chen,Yang Liu,Jianjun Zhao,Bo Li,Jianxiong Yin,Simon See +9 more
TL;DR: DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs, is proposed and a metamorphic mutation strategy to generate new semantically preserved tests is proposed, and multiple extensible coverage criteria as feedback to guide the test generation.
Proceedings ArticleDOI
Hawkeye: Towards a Desired Directed Grey-box Fuzzer
TL;DR: Hawkeye is implemented as a fuzzing framework and evaluated it on various real-world programs under different scenarios, showing that Hawkeye can reach the target sites and reproduce the crashes much faster than state-of-the-art grey-box fuzzers such as AFL and AFLGo.
Proceedings ArticleDOI
DeepStellar: model-based quantitative analysis of stateful deep learning systems
TL;DR: This paper model RNN as an abstract state transition system to characterize its internal behaviors and designs two trace similarity metrics and five coverage criteria which enable the quantitative analysis of RNNs, which are evaluated on four RNN-based systems covering image classification and automated speech recognition.
Proceedings ArticleDOI
FakeSpotter: A simple yet robust baseline for spotting AI-synthesized fake faces
TL;DR: This work proposes a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces, conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector.
Proceedings ArticleDOI
Wuji: automatic online combat game testing using evolutionary deep reinforcement learning
Yan Zheng,Changjie Fan,Xiaofei Xie,Ting Su,Lei Ma,Jianye Hao,Zhaopeng Meng,Yang Liu,Ruimin Shen,Yingfeng Chen +9 more
TL;DR: Wuji is proposed, an on-the-fly game testing framework, which leverages evolutionary algorithms, DRL and multi-objective optimization to perform automatic game testing and demonstrates the effectiveness of Wuji in exploring space and detecting bugs.