A Vision-Based Covert Attack and Hybrid Adversary Detection for Autonomous Vehicles Using Generative Adversarial Network
Bibliographic record
Abstract
Autonomous vehicles (AVs) are advancing fast and are vulnerable to covert, intelligent cyber-attacks. In this paper, a novel covert attack and detection method is introduced. The proposed method is called VCA-HADS, which consists of a vision-based covert attack (VCA) and a hybrid adversary detection system (HADS), which can be applied to a driver assistance system (DAS) of AVs. The VCA would drive the AV out of the lane. Keeping deviation effects hidden, VCA employs a generative adversarial network to manipulate the vision sensor's output such that the AV is positioned on and aligned with the road's center line. To detect malicious behavior of AVs, a HADS is developed using a customized deep neural network, GPS data, and a road map. The DAS is also equipped with a decision-making algorithm to investigate the possibility of a VCA and to warn the driver in high-risk driving situations. To evaluate the performance of VCA-HADS in a multi-agent lane-keeping mission, various 3D driving simulations are performed. Based on the simulation results, the proposed covert attack and detection mechanism are valid and effective.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".