[2603.24821] Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.24821 (cs) [Submitted on 25 Mar 2026] Title:Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting Authors:Alabi Mehzabin Anisha, Guangjing Wang, Sriram Chellappan View a PDF of the paper titled Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting, by Alabi Mehzabin Anisha and 2 other authors View PDF HTML (experimental) Abstract:State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness against adversarial attacks. Recent studies have demonstrated transferability across density-map-based approaches via adversarial patches, but cross-paradigm attacks (i.e., across both density map-based models and point regression-based models) remain unexplored. We introduce a novel adversarial framework that compromises both density map and point regression architectural paradigms through a comprehensive multi-task loss optimization. For point-regression models, we employ scene-density-specific high-confidence logit suppression; for density-map approaches, we use peak-targeted density map suppression. Both are combined with model-agnostic perceptual constraints to ensure that perturbations are effective and imperceptible to the human eye. Extensive experiments demonstrate th...