CriticalFuzz: A critical neuron coverage-guided fuzz testing framework for deep neural networks

Author:

Bai TongtongORCID,Huang SongORCID,Huang Yifan,Wang Xingya,Xia Chunyan,Qu Yubin,Yang Zhen

Funder

Science and Technology Project of Nantong City

State Key Laboratory of Novel Software Technology

Nanjing University

Publisher

Elsevier BV

Reference47 articles.

1. Brian K.S. Isaac-Medina, et al., Unmanned aerial vehicle visual detection and tracking using deep neural networks: A performance benchmark, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.

2. Deep learning-enabled medical computer vision;Esteva;NPJ Digit. Med.,2021

3. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network;Abbas;Appl. Intell.,2021

4. Xing Xu, et al., What machines see is not what they get: Fooling scene text recognition models with adversarial text images, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.

5. Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, Simon See, DeepHunter: a coverage-guided fuzz testing framework for deep neural networks, in: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2019, pp. 146–157.

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