AVATAR: Autonomous Vehicle Assessment Through Testing of Adversarial Patches in Real-Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomy in vehicles is achieved using AI for control and perception tasks. The visual inputs from camera forms the foundation for subsequent control that follows. Existing works have shown adversarial vulnerabilities during AI based visual tasks. One major threat is adversarial patches, which can impact decision making in autonomous vehicles (AVs). Current evaluation methods often utilize static datasets with unrealistic patch placements. This paper proposes a novel framework, AVATAR, to standardize adversarial patch testing and analysis. AVATAR creates a simulation environment, where the patch is integrated with actors in the scene to enhance realism during testing. The vehicle's behaviour is captured as a time-series trace for post-simulation quantitative analysis. Furthermore, we introduce an Adversarial Trace Classifier (ATC) that analyzes these traces to predict the potential presence of adversarial patches. The aim is to detect vulnerabilities in object detection algorithms for the design of robust perception system for AVs. Hence, AVATAR will pave the way for safer deployment of autonomous vehicles in real-world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it