Securing 3D Deep Learning Models: Simple and Effective Defense Against Adversarial Attacks

In recent years, the growing vulnerability of deep neural networks (DNNs) to adversarial attacks has posed sig-nificant challenges in the field of machine learning, particularly in mission-critical applications such as computer vision. As a result, adversarial machine learning has emerged as a crucial research area focused on fortifying deep neural networks against these sophisticated threats. Simultaneously, the use of 3D datasets representing 3D objects has become increasingly important in applications like autonomous driving, robotics, and augmented reality. As the adoption of deep learning networks for processing and classifying both 3D and 2D data grows, concerns over their vulnerability to adversarial attacks have escalated. Despite extensive research on adversarial defense in many areas, adversarial 3D deep learning models remains compara-tively unexplored. This study examines the susceptibility of 3D deep learning to various forms of adversarial attacks, revealing significant weaknesses in current approaches. Our findings demonstrate these attacks, which involve subtle modifications to the data, can significantly degrade the performance of classifiers, leading to misclassification and potentially dangerous outcomes in real-world scenarios. We illustrate that adversarial inputs can compromise prediction performance, with accuracy dropping by over 20%. In response, we propose an efficient defense strategy that does not overburden the learning model with a heavy adversarial training step. Our simplified defense strategy employs only the best classified 3D objects per class and not only restores network classification accuracy to baseline performance but can improve accuracy to above baseline performance in the event of adversarial attacks.