Interpretation Conflict in Helmet Recognition under Adversarial Attack
Why this work is in the frame
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Bibliographic record
Abstract
Humans and Artificial Intelligence (AI) may have observation and interpretation conflicts in collaborative interaction.The adversarial samples make such conflicts more likely to occur in the field of image recognition.However, few studies have been seen combining the human-AI conflict and adversarial attack.This study presents the interpretation conflict due to adversarial samples in the helmet recognition task.A simulation also has been conducted to illustrate this problem.The results show that it should be prudent for the construction industry to land AI applications due to adversarial attacks on image recognition; the adversarial samples easily trigger interpretation conflicts, for example, the logo, graffiti, sticker, and text on helmets; lean construction should be propagated for the preconditions for AI applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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