Deep learning (DL) systems are increasingly applied to safety-critical
domains such as autonomous driving cars. It is of significant importance to
ensure the reliability and robustness of DL systems. Existing testing
methodologies always fail to include rare inputs in the testing dataset and
exhibit low neuron coverage. In this paper, we propose DLFuzz, the frst
differential fuzzing testing framework to guide DL systems exposing incorrect
behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron
coverage and the prediction difference between the original input and the
mutated input, without manual labeling effort or cross-referencing oracles from
other DL systems with the same functionality. We present empirical evaluations
on two well-known datasets to demonstrate its efficiency. Compared with
DeepXplore, the state-of-the-art DL whitebox testing framework, DLFuzz does not
require extra efforts to find similar functional DL systems for
cross-referencing check, but could generate 338.59% more adversarial inputs
with 89.82% smaller perturbations, averagely obtain 2.86% higher neuron
coverage, and save 20.11% time consumption.

本文提出了 DLFuzz, 第一个可引导 DL 系统暴露错误行为的差分模糊测试框架，相比于 DeepXplore 具有更高的神经元覆盖率、更高效的寻找故障输入、更小的干扰等优势。