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Research Paper | Information Technology | United States of America | Volume 11 Issue 3, March 2022 | Popularity: 5.4 / 10

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Adversarial Machine Learning: Attacks and Defense Mechanisms with Respect to AI Security

Rajashekhar Reddy Kethireddy

Abstract: Adversarial machine learning has emerged as a critical area of research at the intersection of artificial intelligence and security, focusing on the vulnerabilities of machine learning models to maliciously crafted inputs. These adversarial attacks exploit the inherent properties of data representations learned by models, causing AI systems to make incorrect or unintended decisions. Such vulnerabilities pose significant threats in security sensitive applications like autonomous vehicles, biometric authentication, and malware detection, where erroneous outputs can lead to severe consequences. This paper provides a comprehensive overview of the landscape of adversarial attacks, including evasion attacks that deceive models during the inference phase and poisoning attacks that compromise models during training. We delve into the methodologies employed by attackers, the theoretical foundations of adversarial examples, and the limitations of current machine learning paradigms in ensuring robustness. Furthermore, we explore various defense mechanisms designed to enhance the resilience of AI models, such as adversarial training, defensive distillation, and robust optimization techniques. By analyzing the effectiveness and limitations of these defenses, we highlight the ongoing challenges in balancing model performance with security. Finally, we discuss future research directions and emphasize the necessity of integrating security considerations into the design and deployment of AI systems to develop robust, reliable, and trustworthy technologies.

Keywords: Adversarial Machine Learning, Security, Attacks, Defense Mechanisms, AI Robustness

Edition: Volume 11 Issue 3, March 2022

Pages: 1634 - 1641

DOI: https://www.doi.org/10.21275/SR220306110542

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