A framework for robust deep learning models against adversarial attacks based on a protection layer approach

Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs). These AEs are meticulously crafted by adversaries, introducing imperceptible perturbations to clean da...

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Bibliographic Details
Main Authors: Tan, Shing Chiang, Mohammed Al-Andoli, Mohammed Nasser, Goh, Pey Yun, Sim, Kok Swee, Lim, Chee Peng
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:http://eprints.utem.edu.my/id/eprint/27255/2/0272917012024103253681.PDF
http://eprints.utem.edu.my/id/eprint/27255/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400453
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