Trembling triggers: exploring the sensitivity of backdoors in DNN-based face recognition

Backdoor attacks against supervised machine learning methods seek to modify the training samples in such a way that, at inference time, the presence of a specific pattern (trigger) in the input data causes misclassifications to a target class chosen by the adversary. Successful backdoor attacks have...

Full description

Saved in:

Bibliographic Details
Published in:	EURASIP Journal on Information Security Vol. 2020; no. 1; pp. 1 - 15
Main Authors:	Pasquini, Cecilia, Böhme, Rainer
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 23.06.2020 Springer Nature B.V SpringerOpen
Subjects:	Adversarial machine learning Artificial neural networks Backdoor attacks Communications Engineering Dependable deep learning for security-oriented applications Engineering Face recognition Facial recognition technology Image processing Inference Machine learning Networks Neural networks Object recognition Security Science and Technology Signal processing Signal,Image and Speech Processing Systems and Data Security Training Neural networks Adversarial machine learning Backdoor attacks
ISSN:	2510-523X, 1687-4161, 2510-523X, 1687-417X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!