A Comprehensive Review on Adversarial Attack Detection Analysis in Deep Learning

Authors

  • Soni Kumari  Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India
  • Sheshang Degadwala  

DOI:

https://doi.org/10.32628/CSEIT2361054

Keywords:

Adversarial Attacks, Deep Learning, Detection Techniques, Machine Learning, Interpretability, Explainable Artificial Intelligence (XAI), Reinforcement Learning.

Abstract

This comprehensive review investigates the escalating concern of adversarial attacks on deep learning models, offering an extensive analysis of state-of-the-art detection techniques. Encompassing traditional machine learning methods and contemporary deep learning approaches, the review categorizes and evaluates various detection mechanisms while addressing challenges such as the need for benchmark datasets and interpretability. Emphasizing the crucial role of explaining ability and trustworthiness, the paper also explores emerging trends, including the integration of technologies like explainable artificial intelligence (XAI) and reinforcement learning. By synthesizing existing knowledge and outlining future research directions, this review serves as a valuable resource for researchers, practitioners, and stakeholders seeking a nuanced understanding of adversarial attack detection in deep learning.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Soni Kumari, Sheshang Degadwala, " A Comprehensive Review on Adversarial Attack Detection Analysis in Deep Learning " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.319-325, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT2361054