Strengthening AI Security: An Exploration of Advanced Adversarial Defense Techniques

This research focuses on improving model robustness against adversarial attacks, emphasizing the effectiveness of adversarial training. Traditional defense methods, though widely used, were found to be less effective, leading to the adoption of adversarial training for its superior performance and practical benefits. A significant contribution of this work is the combination of autoencoders with adversarial training, which has shown promising results in strengthening model resilience. Various models have been evaluated, and ongoing efforts include performance enhancement through hyperparameter tuning and dataset optimization. Additionally, the exploration of Kolmogorov-Arnold Networks (KANs), which utilize the Kolmogorov-Arnold representation theorem for better generalization and robustness, is discussed. However, results suggest that KANs are less effective compared to autoencoders in defending against adversarial attacks while maintaining computational efficiency which is the good thing. Consequently, autoencoders currently demonstrate the best performance in adversarial training scenarios.