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Home > Archive>Volume 50, Issue 10, 2022 >1405-1413. DOI:10.11908/j.issn.0253-374x.22266
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Robustness Verification of Deep Neural Networks on High-speed Rail Operating Environment Recognition
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School of Software Engineering， Tongji University， Shanghai 201804， China

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TP389.1

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    Abstract:

    The implementation of DeepTRE was improved to adapt to large-scale dataset scenarios， which greatly reduces the space complexity of DeepTRE when retaining the excellent verification ability of DeepTRE. The improved DeepTRE was evaluated in the high-speed rail operating environment recognition scenarios and was compared with other mainstream verification tools， i.e.，DLV and SafeCV. The experimental results show that the memory usage of the improved DeepTRE tool is significantly lower than that of the original DeepTRE tool. Compared with other neural network verification tools， the improved DeepTRE tool has better verification effect on the premise of faster verification speed.

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GAO Zhen, SU Yu, HOU Xiaoxue, FANG Pei, ZHANG Miaomiao. Robustness Verification of Deep Neural Networks on High-speed Rail Operating Environment Recognition[J].同济大学学报(自然科学版),2022,50(10):1405~1413

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  • Received:May 10,2022
  • Online: November 03,2022
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