Current Volume 8
Addressing the Vulnerability of Neural Networks to Adversarial Attacks: Challenges, Implications and Solutions for Safety-Critical Applications
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.
Citations
IRE Journals:
Nagaraj C , Dr. Hemalatha B , Dr. K. Jamberi
"Addressing the Vulnerability of Neural Networks to Adversarial Attacks: Challenges, Implications and Solutions for Safety-Critical Applications" Iconic Research And Engineering Journals Volume 8 Issue 1 2024 Page 43-47
IEEE:
Nagaraj C , Dr. Hemalatha B , Dr. K. Jamberi
"Addressing the Vulnerability of Neural Networks to Adversarial Attacks: Challenges, Implications and Solutions for Safety-Critical Applications" Iconic Research And Engineering Journals, 8(1)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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