Robustness and Security in Deep Learning: Adversarial Attacks and Countermeasures

Abstract

Deep learning models have demonstrated remarkable performance across various domains, yet their susceptibility to adversarial attacks remains a significant concern. In this study, we investigate the effectiveness of three defense mechanisms—Baseline (No Defense), Adversarial Training, and Input Preprocessing—in enhancing the robustness of deep learning models against adversarial attacks. The baseline model serves as a reference point, highlighting the vulnerability of deep learning systems to adversarial perturbations. Adversarial Training, involving the augmentation of training data with adversarial examples, significantly improves model resilience, demonstrating higher accuracy under both Fast Gradient Sign Method (FGSM) and Iterative Gradient Sign Method (IGSM) attacks. Similarly, Input Preprocessing techniques mitigate the impact of adversarial perturbations on model predictions by modifying input data before inference. However, each defense mechanism presents trade-offs in terms of computational complexity and performance. Adversarial Training requires additional computational resources and longer training times, while Input Preprocessing techniques may introduce distortions affecting model generalization. Future research directions may focus on developing more sophisticated defense mechanisms, including ensemble methods, gradient masking, and certified defense strategies, to provide robust and reliable deep learning systems in real-world scenarios. This study contributes to a deeper understanding of defense mechanisms against adversarial attacks in deep learning, highlighting the importance of implementing robust strategies to enhance model resilience.