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Addressing the Vulnerability of Neural Networks to Adversarial Attacks: Challenges, Implications and Solutions for Safety-Critical Applications
Subject area: Science,Engineering and Technology · Area of research: Neural Networks
Abstract
Neural networks have demonstrated unparalleled success in various domains, yet challenges persist regarding their robustness and generalization capabilities. A significant concern is their vulnerability to adversarial attacks, where imperceptible perturbations in input data can cause erroneous predictions. This paper offers a comprehensive examination of the phenomenon of adversarial attacks on neural networks. Through empirical analysis and theoretical insights, we elucidate the mechanisms underlying these attacks and their implications for real-world deployment. Additionally, we investigate state-of-the-art defense mechanisms and mitigation strategies aimed at bolstering the robustness of neural networks against adversarial manipulation. By addressing these challenges head-on, we aim to contribute to the advancement of neural network security and reliability, facilitating their safe and effective integration into safety-critical systems.
Keywords
Neural networks, Adversarial attacks, Robustness, Generalization, Safety-critical applications, Defense mechanisms, Mitigation strategies.
How to cite this paper
@article{1705997,
author = {Nagaraj C, Dr. Hemalatha B, Dr. K. Jamberi},
title = {Addressing the Vulnerability of Neural Networks to Adversarial Attacks: Challenges, Implications and Solutions for Safety-Critical Applications},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {1},
pages = {43-47},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705997.pdf},
abstract = {Neural networks have demonstrated unparalleled success in various domains, yet challenges persist regarding their robustness and generalization capabilities. A significant concern is their vulnerability to adversarial attacks, where imperceptible perturbations in input data can cause erroneous predictions. This paper offers a comprehensive examination of the phenomenon of adversarial attacks on neural networks. Through empirical analysis and theoretical insights, we elucidate the mechanisms underlying these attacks and their implications for real-world deployment. Additionally, we investigate state-of-the-art defense mechanisms and mitigation strategies aimed at bolstering the robustness of neural networks against adversarial manipulation. By addressing these challenges head-on, we aim to contribute to the advancement of neural network security and reliability, facilitating their safe and effective integration into safety-critical systems.},
keywords = {Neural networks, Adversarial attacks, Robustness, Generalization, Safety-critical applications, Defense mechanisms, Mitigation strategies.},
month = {July},
}