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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".