Adversarial Training Methods for Deep Learning: A Systematic Review

• Published in: Algorithms Vol. 15; no. 8; p. 283
• Main Authors: Zhao, Weimin, Alwidian, Sanaa, Mahmoud, Qusay H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: adversarial attack generation; adversarial attacks; adversarial machine learning; adversarial samples; adversarial training; Algorithms; Analysis; Artificial neural networks; Computational linguistics; Deep learning; deep neural network; Efficiency; Identification methods; Language processing; Literature reviews; Machine learning; Natural language interfaces; Neural networks; Optimization; Research methodology; Robustness; State-of-the-art reviews; Strategy; Systematic review; Training

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training s...