[2603.24232] Attack Assessment and Augmented Identity Recognition for Human Skeleton Data

Computer Science > Machine Learning arXiv:2603.24232 (cs) [Submitted on 25 Mar 2026] Title:Attack Assessment and Augmented Identity Recognition for Human Skeleton Data Authors:Joseph G. Zalameda, Megan A. Witherow, Alexander M. Glandon, Jose Aguilera, Khan M. Iftekharuddin View a PDF of the paper titled Attack Assessment and Augmented Identity Recognition for Human Skeleton Data, by Joseph G. Zalameda and 4 other authors View PDF Abstract:Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification from LiDAR based skeleton data requires time consuming and expensive data acquisition for each subject identity. Recently, Assessment and Augmented Identity Recognition for Skeletons (AAIRS) has been used to train Hierarchical Co-occurrence Networks for Person Identification (HCN-ID) with small LiDAR based skeleton data sets. However, AAIRS does not evaluate robustness of HCN-ID to adversarial attacks or inoculate the model to defend against such attacks. Popular perturbation-based approaches to generating adversarial attacks are constrained to targeted perturbations added to real training samples, which is not ideal for inoculating models with small training sets. Thus, we propose Attack-AAIRS, a novel addition to the AAIRS framework. Attack-AAIRS leverages a small real data set and a GAN generated synthetic data set to assess and improve model robustness against unseen adversarial attacks. ...