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Towards Certifying the Asymmetric Robustness for Neural Net..:
Li, Changjiang
;
Ji, Shouling
;
Weng, Haiqin
...
IEEE Transactions on Dependable and Secure Computing. 19 (2022) 6 - p. 3987-4001 , 2022
Link:
https://doi.org/10.1109/tdsc.2021.3116105
RT Journal T1 <Title>Towards Certifying the Asymmetric Robustness for Neural Networks: Quantification and Applications</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1109_tdsc.2021.3116105&Exemplar=1&LAN=DE A1 Li, Changjiang A1 Ji, Shouling A1 Weng, Haiqin A1 Li, Bo A1 Shi, Jie A1 Beyah, Raheem A1 Guo, Shanqing A1 Wang, Zonghui A1 Wang, Ting PB Institute of Electrical and Electronics Engineers (IEEE) YR 2022 SN 1545-5971 SN 1941-0018 SN 2160-9209 JF IEEE Transactions on Dependable and Secure Computing VO 19 IS 6 SP 3987 OP 4001 LK http://dx.doi.org/https://doi.org/10.1109/tdsc.2021.3116105 DO https://doi.org/10.1109/tdsc.2021.3116105 SF ELIB - SuUB Bremen
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