Xiaofei Xie

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.

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.

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.

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.

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.