DCN: Detector-Corrector Network Against Evasion Attacks on Deep Neural Networks

Wen, Jing; Hui, Lucas Chi Kwong; Yiu, Siu-Ming; Zhang, Ruoqing

doi:10.1109/dsn-w.2018.00066

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Cited by 4 publications

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“…Meanwhile, with the wide adoption of machine learning based applications, the reliability and security of machine learning-based software also attract much research attention [13]- [16]. This line of work mainly focuses on understanding and addressing inherent defects in DL models.…”

Section: Related Workmentioning

confidence: 99%

Detecting Deep Neural Network Defects with Data Flow Analysis

Gu¹,

Xu²,

Zhou³

et al. 2019

Preprint

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Deep neural networks (DNNs) are shown to be promising solutions in many challenging artificial intelligence tasks. However, it is very hard to figure out whether the low precision of a DNN model is an inevitable result, or caused by defects. This paper aims at addressing this challenging problem. We find that the internal data flow footprints of a DNN model can provide insights to locate the root cause effectively. We develop DeepMorph (DNN Tomography) to analyze the root cause, which can guide a DNN developer to improve the model.

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Section: Related Workmentioning

confidence: 99%