Vol. 2 No. 1 (2022): Advances in Deep Learning Techniques
Articles

Adversarial Training Techniques in Deep Learning: Analyzing Adversarial Training Techniques to Enhance the Robustness of Deep Learning Models Against Adversarial Attacks

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  • Prof. Wei Chen,
  • Gopalakrishnan Arjunan
Prof. Wei Chen
Associate Professor of Computational Intelligence, Tsinghua University, Beijing, China
Gopalakrishnan Arjunan
AI/ML Engineer at Accenture, Bangalore, India
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Published 28-04-2022

Keywords

  • Adversarial Training,
  • Deep Learning,
  • Adversarial Attacks,
  • Robustness,
  • Neural Networks,
  • Gradient Descent,
  • Defense Mechanisms,
  • Transferability,
  • Attack Strategies,
  • Model Interpretability
    • ...More
    Less

How to Cite

[1]
P. W. Chen and G. Arjunan, “Adversarial Training Techniques in Deep Learning: Analyzing Adversarial Training Techniques to Enhance the Robustness of Deep Learning Models Against Adversarial Attacks”, Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 15–26, Apr. 2022.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Adversarial attacks pose a significant threat to the reliability of deep learning models. Adversarial training has emerged as a promising approach to enhance the robustness of these models. This paper provides a comprehensive analysis of adversarial training techniques in deep learning, aiming to understand their effectiveness in improving model robustness against adversarial attacks. We discuss the fundamental concepts of adversarial attacks and adversarial training, review key adversarial training methods, and analyze their impact on model performance and robustness. Additionally, we highlight challenges and future research directions in this area.

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