Defending Machine Learning Systems: Adversarial Attacks and Robust Defenses in the U.S. and Asia
Keywords:
Adversarial Attacks, Machine Learning Security, Data Poisoning, Defense Mechanisms/Adversarial Training.Abstract
This paper examines the escalating threat of adversarial attacks against machine learning systems, focusing on the distinct landscapes of the U.S. and Asia. As machine learning models become increasingly integrated into critical infrastructure and decision-making processes, their vulnerability to adversarial manipulation poses significant security risks. This study investigates various types of adversarial attacks, including data poisoning, where malicious actors inject corrupted data into training sets to compromise model integrity, and evasion attacks, which involve crafting subtle perturbations to input data to induce misclassification. The research explores prominent defense mechanisms, such as adversarial training, which enhances model robustness by incorporating adversarial examples into the training process, and regularization techniques that mitigate overfitting and improve generalization. Furthermore, the paper analyzes the divergent regulatory standards and technical capabilities between the U.S. and Asian markets, highlighting their influence on the deployment and efficacy of secure AI systems. By comparing and contrasting these two regions, the study aims to identify best practices and inform the development of robust defense strategies against adversarial attacks. The findings contribute to a deeper understanding of the challenges and opportunities in securing machine learning systems within diverse technological and regulatory contexts, ultimately promoting the development of more resilient and trustworthy AI applications.