Adversarial Geometric Attacks for 3D Point Cloud Object Tracking
Bibliographic record
Abstract
3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking models. However, existing adversarial attack methods for 3D PCOT seldom leverage the geometric structure of point clouds and often overlook the transferability of attack strategies. To address these limitations, this paper proposes an adversarial geometric attack method tailored for 3D PCOT, which includes a point perturbation attack module (non-isometric transformation) and a rotation attack module (isometric transformation). First, we introduce a curvature-aware point perturbation attack module that enhances local transformations by applying normal perturbations to critical points identified through geometric features such as curvature and entropy. Second, we design a Thompson sampling-based rotation attack module that applies subtle global rotations to the point cloud, introducing tracking errors while maintaining imperceptibility. Additionally, we design a fused loss function to iteratively optimize the point cloud within the search region, generating adversarially perturbed samples. The proposed method is evaluated on multiple 3D PCOT models and validated through black-box tracking experiments on benchmarks. For P2B, white-box attacks on KITTI reduce the success rate from 53.3% to 29.6% and precision from 68.4% to 37.1%. On NuScenes, the success rate drops from 39.0% to 27.6%, and precision from 39.9 to 26.8%. Black-box attacks show a transferability, with BAT showing a maximum 47.0% drop in success rate and 47.2% in precision on KITTI, and a maximum 22.5% and 27.0% on NuScenes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".