Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Xianjing Fang"'
Publikováno v:
PeerJ Computer Science, Vol 7, p e702 (2021)
Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work. In a more practical situation, the a
Externí odkaz:
https://doaj.org/article/cf28bed5d29744a4938e0033b178602e
Publikováno v:
PeerJ Computer Science, Vol 7, p e702 (2021)
PeerJ Computer Science
PeerJ Computer Science
Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work. In a more practical situation, the a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2a561fd191e43a5dce362990bcf0603
https://peerj.com/articles/cs-702.pdf
https://peerj.com/articles/cs-702.pdf
Autor:
Dan Li, Fang Bao, Xianjing Fang, Liwei Zou, Rongmiao Qi, Suisheng Zheng, Lianzi Su, Zhimin Zhai, Longsheng Wang
Publikováno v:
Oncotarget
// Liwei Zou 1, * , Lianzi Su 1, * , Rongmiao Qi 1, * , Fang Bao 1 , Xianjing Fang 1 , Longsheng Wang 1 , Zhimin Zhai 2 , Dan Li 3 and Suisheng Zheng 1, 4 1 Department of Radiology, The Second Hospital of Anhui Medical University, Anhui, China 2 Depa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49988a71bc0f3f028ad36709df2af744
https://doi.org/10.18632/oncotarget.19104
https://doi.org/10.18632/oncotarget.19104
Publikováno v:
IEEE Transactions on Cloud Computing. :1-1
In the cloud environment, it brings better reliability and robustness with geographically distributed datacenters. As the growth of large-scale applications in geo-distributed cloud systems, the resource demand from different areas increases violentl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02423eaf2f208d744d7f695ff39a2c53
https://doi.org/10.1109/tcc.2018.2808351
https://doi.org/10.1109/tcc.2018.2808351