A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

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: Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim, Pey Yun Goh, Chee Peng Lim
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
adversarial examples
security
adversarial attacks
adversarial examples detection
Online Access:https://ieeexplore.ieee.org/document/10400453/

Internet

https://ieeexplore.ieee.org/document/10400453/

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