DeepXplore: automated whitebox testing of deep learning systems

摘要

Many of us use trustworthy electronic systems, from self-driving car owners to online bankers and shoppers. How should real-life computer systems be methodically tested for nearly all potential faults and malware threats, to instill confidence in users? Pei et al. present DeepXplore, the first system of its kind that uses deep learning (DL) techniques to design and exhaustively test for impending malware threats and defects in software. The authors identify two major drawbacks of current deep neural network (DNN) testing strategies: (1) the exorbitant human endeavors to create accurate behaviors and classifications for specific chores, and (2) the marginal assessment of various behavioral rules. Consequently, they present DeepXplore, a programmed whitebox archetype for methodically assessing inaccurate situation actions in DNNs, such as self-reliant cars bumping into shield fences.