Coverage-guided fuzzing for feedforward neural networks

• Main Authors: XIE, Xiaofei, CHEN, Hongxu, LI, Yi, MA, Lei, LIU, Yang, ZHAO, Jianjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7067; https://ink.library.smu.edu.sg/context/sis_research/article/8070/viewcontent/ASE.2019.00127.pdf
• Institution: Singapore Management University

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support multiple built-in configurations which are easy to extend. We evaluated DeepHunter on two popular datasets and the results demonstrate the effectiveness of DeepHunter in achieving coverage increase and detecting real defects. A video demonstration which showcases the main features of DeepHunter can be found at https://youtu.be/s5DfLErcgrc.