In the field of photogrammetry and remote sensing, three-dimensional point cloud data—acquired via airborne lidar, satellite stereo imagery, or terrestrial laser scanning—has emerged as the core data type for applications such as terrain mapping, urban modeling, disaster monitoring, and infrastructure inspection. The accuracy and reliability of these applications largely depend on the precise interpretation of point-cloud data. However, existing deep neural networks exhibit pronounced vulnerability to adversarial perturbation attacks that can mislead models into misclassifying targets, potentially leading to catastrophic consequences. Most of the current point cloud–perturbation methods rely on the white box–attack assumption, which is difficult to achieve in real application scenarios. To address this gap, this paper focuses on enhancing the transferability of point-cloud perturbations—the ability of adversarial samples generated for one model to mislead other uninvolved models. A novel adversarial point cloud–generation method, momentum-based decompose perturbation (MBDP) is proposed. The MBDP method decomposes adversarial perturbations into two orthogonal subperturbations. By integrating a momentum-based iterative fast sign algorithm, the MBDP method synchronously optimizes both subperturbations to generate adversarial samples that lie far from the original model’s decision boundary while maintaining cross-model generalizability. On both real-world remote sensing data sets and synthetic data sets, the MBDP method achieves great performance. By exposing cross-model vulnerabilities in photogrammetric point clouds, this work equips developers with a diagnostic tool to audit and subsequently harden artificial-intelligence–driven mapping and monitoring systems, laying the groundwork for more reliable geospatial products in disaster response, urban planning, and environmental conservation.
Liu et al. (Thu,) studied this question.