DSpace at KOASAS: FriendNet Backdoor: Indentifying Backdoor Attack that is safe for Friendly Deep Neural Network

DSpace at KOASAS

College of Engineering(공과대학)School of Computing(전산학부)CS-Conference Papers(학술회의논문)

FriendNet Backdoor: Indentifying Backdoor Attack that is safe for Friendly Deep Neural Network

Cited 0 time in webofscience

Cited 0 time in scopus

Hit : 419
Download : 0

Export

Kwon, Hyun / Yoon, Hyunsoo researcher / Park, Ki-Woong

Publisher: ACM’s International Conference Proceedings Series

Issue Date: 2020-01-13

Language: English

Citation: The 3rd International Conference on Software Engineering and Information Management (ICSIM 2020)

URI: http://hdl.handle.net/10203/271943

Appears in Collection: CS-Conference Papers(학술회의논문)

Files in This Item: There are no files associated with this item.

Display Full Item Record

qr_code

트윗하기

KOASAS

Knowledge Service Development Team, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea. T. 82-42-350-4493 Email. koasas@kaist.ac.kr
Copyright © 2016. Korea Advanced Institute of Science and Technology. All Rights Reserved.

KOASAS

KOASAS

Browse

FriendNet Backdoor: Indentifying Backdoor Attack that is safe for Friendly Deep Neural Network

KOASAS

Communities & Collections