Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversari...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 4307 - 4316
Hlavní autori:	Dong, Yinpeng, Pang, Tianyu, Su, Hang, Zhu, Jun
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2019
Predmet:	Categorization Codes Computational modeling Computer vision Deep learning Glass box Kernel Pattern recognition Perturbation methods Recognition: Detection Retrieval Robustness Security
ISSN:	1063-6919
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