Mitigating Adversarial Attacks on AI Models

Abstract

Adversarial attacks on artificial intelligence (AI) models pose a significant threat to their reliability, security, and practical deployment across various domains. These attacks exploit vulnerabilities in AI systems by introducing imperceptible perturbations to input data, leading to incorrect predictions. The severity of adversarial attacks ranges from simple modifications in images to sophisticated evasion techniques that deceive even state-of-the-art models. In this research paper, we provide an in-depth analysis of adversarial attacks, their impact on AI models, and state-of-the-art mitigation techniques. Our study explores various defensive strategies, including adversarial training, robust optimization, input preprocessing, and model architecture enhancements. We conduct experimental evaluations on standard AI models to assess the effectiveness of different mitigation approaches against adversarial attacks. Our results indicate that while no single mitigation strategy provides complete immunity, combining multiple approaches significantly improves robustness. The findings underscore the necessity of continuous innovation in AI security to safeguard AI applications against evolving adversarial threats.