Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation

Universal Adversarial Perturbations are image-agnostic and model-independent noise that when added with any image can mislead the trained Deep Convolutional Neural Networks into the wrong prediction. Since these Universal Adversarial Perturbations can seriously jeopardize the security and integrity...

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Bibliographische Detailangaben

Hauptverfasser:	Sadi, Mehdi, Talukder, B. M. S. Bahar, Mishty, Kaniz, Rahman, Md Tauhidur
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Cryptography and Security Computer Science - Learning
Online-Zugang:	Volltext bestellen
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