Adversarial Attacks on Machine Learning Models: Challenges and Defense Mechanisms
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Abstract
Adversarial attacks pose a significant threat to the security of machine learning models. This paper examines the types of adversarial attacks and their impact on model performance. We also explore various defense mechanisms, including adversarial training and input preprocessing, to mitigate these risks. The study highlights the importance of robust security measures in safeguarding AI systems.
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