A real-time privacy-sensitive data hiding approach based on chaos cryptography
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
A multimedia surveillance system aims to provide security and safety of people in a monitored space. However, due to the nature of surveillance, privacy-sensitive information, such as face, gait and other physical parameters based on the captured media from multiple sensors, can be revealed without the concern of the people. This is a major concern in recent days. Therefore, it is desirable to have such mechanism that can hide privacy-sensitive information as much as possible, yet supporting effective surveillance tasks. In this paper, we propose a chaos cryptography based data hiding approach that can be applied on selected regions of interest (ROIs) in video camera footage, which contains privacy-sensitive data. Our approach also supports multiple levels of abstraction of data hiding depending on the role of the authorized user. In order to evaluate the suitability of this approach, we applied our algorithm on some video camera footage and observed that our approach is computationally efficient and applicable for real-time video surveillance tasks.
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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 | 0.002 | 0.001 |
| 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