Security and privacy protection for automated video surveillance
Bibliographic record
Abstract
In this paper, we present an automated video surveillance system designed to 1) ensure efficient selective storage of data, 2) provide means for enhancing privacy protection, and 3) secure visual data against malicious attacks. The proposed solution is a 3-module system processing captured video data before storage. Salient motion detection is used to retain relevant sequences and identify regions of interest with potential privacy-sensitive details. Then, an invertible and secure privacy preserving process is performed using a DCT-based scrambling technique on selected regions. To secure visual data and allow for data authentication, a self-embedding watermarking technique is applied on each image sequence. It offers the capability of proving authenticity as well as locating manipulated regions. Furthermore, this technique is also able to recover and reconstruct a good approximation of original lost content. In addition to a low computational complexity, simulation results show the effectiveness of the whole system in achieving its goals in terms of security and privacy enhancement of automated video surveillance data.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".