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...

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Vydané v:	EURASIP Journal on Information Security Ročník 2020; číslo 1; s. 1 - 15
Hlavní autori:	Pasquini, Cecilia, Böhme, Rainer
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 23.06.2020 Springer Nature B.V SpringerOpen
Predmet:	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
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