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)
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