Spatio-temporal Explanation for Adversarial-Aware Cloud Vision AI Services
Bibliographic record
Abstract
Building upon our previous work on trustworthy explanation of cloud AI services published in IEEE Transactions on Cloud Computing (doi: 10.1109/TCC.2024.3398609), this extension proposes a spatio-temporal explanation framework to enhance the adversarial awareness of cloud vision services. Along with the increasing adoption of vision models for learning tasks on video streams, adversarial attack on video becomes a severe source of degrading the cloud vision service's efficacy. The explanation of the spatial features of local image frames and temporal properties along the timeline of frames enables transparency and awareness of the impact source under adversarial attacks. This extension addresses two critical challenges, namely (1) the development of unified spatiotemporal explanations that can handle both image and video models; and (2) the assessment of the vulnerability of cloud vision models to adversarial attacks and their impact on explanation trustworthiness. The proposed extension research will integrate adversarial robustness assessment with spatio-temporal feature analysis, enabling unified explanation pipelines for the multi-tasks of cloud vision services.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".