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A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability [article]

Xiaowei Huang and Daniel Kroening and Wenjie Ruan and James Sharp and Youcheng Sun and Emese Thamo and Min Wu and Xinping Yi
2020 arXiv   pre-print

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2019 pre-print
arXiv:1812.08342v3 fatcat:y7sj77xtlvfcdilscsgei6mtj4
2018 pre-print
arXiv:1812.08342v2 fatcat:p6kct6h7obcophltgmx5ke4a6e
2019 pre-print
arXiv:1812.08342v4 fatcat:sp2yqh2obzccze6wkmidvr2pna
2018 pre-print
arXiv:1812.08342v1 fatcat:32pnfmuafnep3j6izfpuwitera
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few
more » ... years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017.
arXiv:1812.08342v5 fatcat:awndtbca4jbi3pcz5y2d4ymoja
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Citation

Xiaowei Huang and Daniel Kroening and Wenjie Ruan and James Sharp and Youcheng Sun and Emese Thamo and Min Wu and Xinping Yi. "A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability." arXiv (2020)