An Empirical Study of Counterexample-Guided Fuzzing for Neural Networks Verification

The uncertainty of intelligence software makes it impossible for even developers to determine the output corresponding to each input. Therefore, it is particularly important to find a way to find intelligence software errors efficiently. Fuzzing testing technology is considered to be an effective technology for discovering software vulnerabilities, The selected test case generation strategy will affect its testing efficiency. In this article, we propose a counterexample-guided fuzzing method. According to the generated counterexamples, the scope of the sample space is continuously reduced, so as to find more counterexamples in a limited number of samples and improve the ability to uncover mistakes. In our research, The ability about revealing errors of test object has increased by 20%.