Adversarial Training Techniques in Deep Learning: Analyzing Adversarial Training Techniques to Enhance the Robustness of Deep Learning Models Against Adversarial Attacks
Keywords:
Adversarial Training, Deep Learning, Adversarial Attacks, Robustness, Neural Networks, Gradient Descent, Defense Mechanisms, Transferability, Attack Strategies, Model InterpretabilityAbstract
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.
Downloads
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
