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Analysis of Adversarial Attacks and Defense Mechanisms on Machine Learning Models

Abstract:

A comprehensive literature review analyzing various adversarial attack vectors on machine learning models and evaluating the effectiveness of current defense mechanisms.

Research Goals:

• •Analyze common adversarial attack methods
• •Evaluate defense mechanism effectiveness
• •Identify gaps in current security measures

Method:

Systematic literature review of recent publications in the field of adversarial machine learning.

Results:

Identified key vulnerabilities in standard CNN architectures and highlighted the trade-offs between model robustness and accuracy.