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1 Ergebnisse
1
Stateful Defenses for Machine Learning Models Are Not Yet S..:
, In:
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
,
Feng, Ryan
;
Hooda, Ashish
;
Mangaokar, Neal
... - p. 786-800 , 2023
Link:
https://dl.acm.org/doi/10.1145/3576915.3623116
RT T1
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
: T1
Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks
UL https://suche.suub.uni-bremen.de/peid=acm-3623116&Exemplar=1&LAN=DE A1 Feng, Ryan A1 Hooda, Ashish A1 Mangaokar, Neal A1 Fawaz, Kassem A1 Jha, Somesh A1 Prakash, Atul PB ACM YR 2023 K1 adversarial examples K1 black-box attacks K1 machine learning K1 security K1 stateful defenses K1 Security and privacy K1 Computing methodologies K1 Machine learning SP 786 OP 800 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3576915.3623116 DO https://dl.acm.org/doi/10.1145/3576915.3623116 SF ELIB - SuUB Bremen
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