Universal Closed-Box Adversarial Attack for Trajectory Representation via Controlling High-Dimensional Iterative Constraints

With the proliferation of trajectory data generated by many Internet of Things (IoT) devices in the AI-driven transportation field, trajectory representation is crucial for extracting individual behavioral characteristics from intelligent IoT systems. Neural network-based trajectory representation learning methods excel at obtaining consistent representations of individual movements and mining spatio-temporal autocorrelation features of trajectories. However, existing methods achieve high accuracy in reliable experimental data, and their high performance is not always available in real-world wild scenarios, which is not available to reveal the performance bounds of trajectory representation learning models when confronted with real-world phenomena. Given this, we propose a universal trajectory adversarial attack framework that investigates the robustness vulnerabilities of trajectory representation learning models by generating adversarial trajectory examples. We reveal the impact of the attack surface in practical deployment and the adversarial perturbation budget on the trajectory adversarial attack performance. Guided by the new framework, we propose a universal black-box adversarial attack for trajectories, named Universal Trajectory Customized Iteration Attack (UTCIA). Specifically, we simulate trajectories through single-point perturbations to obtain a vulnerability-dependent set of victim points, selecting and perturbing only a small subset of points to degrade the entire model performance. Furthermore, we aggregate the discrete set of salient target trajectory points into a high-dimensional space for iterative direction estimation, and relax the constraint region to further compress noise. We generate adversarial trajectory examples for two trajectory representation downstream tasks with fundamentally different objectives using multiple large-scale real-world datasets. Extensive experiments demonstrate the feasibility and generalizability of our proposed framework in exploring the robustness vulnerabilities of trajectory representation learning models.