Adversarial Machine Learning in Cyber Social Security
Bibliographic record
Abstract
Machine learning plays a crucial role in autonomous vehicles, particularly in driver assistance technologies that enhance driving efficiency or eliminate the need for human intervention. One critical application is traffic sign recognition, which helps vehicles adjust their driving behavior based on environmental conditions. However, these systems are vulnerable to adversarial attacks, such as evasion and poisoning, which can compromise security, system integrity, and passenger safety. These attacks target machine learning models by corrupting training processes, leading to misclassifications and false information. Given that these models rely on sensor-acquired images, it is essential to investigate potential threats that could impact autonomous vehicles. Therefore, this research conducts an experimental analysis of Black-Box Adversarial Machine Learning Attacks, specifically Zeroth-Order Optimization (ZOO), on a Convolutional Neural Network (CNN) for traffic sign recognition. By leveraging optimization techniques, the study aims to assess the risks posed by these attacks on autonomous vehicles and their implications in Multi-Domain Operation (MDO) contexts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".