DSADA: Detecting Spoofing Attacks in Driver Assistance Systems Using Objects’ Spatial Shapes
Bibliographic record
Abstract
Object detection algorithms suffer from a perceptual vulnerability where they cannot differentiate between counterfeit and real objects. In this paper, we investigate the perceptual vulnerability in advanced driver assistance systems (ADAS) when faced with physical and digital spoofing attacks. To address this vulnerability, we propose a method named DSADA (Detecting Spoofing Attacks in Driver Assistance) to mitigate creation and misclassification spoofing attacks against object detection algorithms utilizing the LiDAR point clouds and objects’ spatial shapes. DSADA receives the outcomes of the object detection algorithm along with the corresponding LiDAR point clouds for each scene. DSADA exploits the spatial shapes of objects obtained from the point clouds to cross-validate the outcomes of the object detection algorithm. Any discrepancy results in generating an alert to warn about the spoofing attack. We analyze defense-aware and unaware attacks against DSADA. The evaluation results show the effectiveness of the suggested method with a true positive rate of 100% and a low false positive rate of only 3.97%. The comparative evaluation validates that the suggested method identifies a broader range of spoofed objects, including projected, displayed and printed ones, while narrowing the scope of potential attacks to familiar objects in the driving context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".