Efficient Defense Against Adversarial Attacks

Abstract

Adversarial attacks are a significant vulnerability for deep learning models, particularly Convolutional Neural Networks (CNNs), which are widely employed in image classification and object detection. These attacks involve crafting imperceptible perturbations to input data that mislead CNNs into making incorrect predictions, posing risks in critical areas such as autonomous driving, security, and healthcare. This paper focuses on understanding the nature of adversarial attacks on CNNs, including white-box attacks, where attackers have full knowledge of the model’s parameters, and black-box attacks, where attackers have limited or no access to the model’s architecture. Common attack techniques such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) and many more are reviewed to illustrate how CNNs can be compromised. In response to these threats, we explore various defense mechanisms aimed at increasing CNN robustness. Adversarial training, which incorporates adversarial examples during the training process, is a prominent defense. Other approaches, like input preprocessing, gradient obfuscation, and randomization techniques, are also discussed. This work emphasizes the trade-off between the efficiency of these defenses and their ability to protect CNNs without significantly increasing computational costs.