Privacy Protected Surveillance Using Secure Visual Object Coding
Bibliographic record
Abstract
This paper presents the Secure Shape and Texture SPIHT (SecST-SPIHT) scheme for secure coding of arbitrarily shaped visual objects. The scheme can be employed in a privacy protected surveillance system, whereby visual objects are encrypted so that the content is only available to authorized personnel with the correct decryption key. The secure visual object coder employs shape and texture set partitioning in hierarchical trees (ST-SPIHT) along with a novel selective encryption scheme for efficient, secure storage and transmission of visual object shape and textures. The encryption is performed in the compressed domain and does not affect the rate-distortion performance of the coder. A separate parameter for each encrypted object controls the strength of the encryption versus required processing overhead. Security analyses are provided, demonstrating the confidentiality of both the encrypted and unencrypted portions of the secured output bit-stream, effectively securing the entire object shape and texture content. Experimental results showed that no object details are revealed to attackers who do not possess the correct decryption key. Using typical parameter values and output bit-rates, the SecST-SPIHT coder is shown to require encryption on less than 5% of the output bit-stream, a significant reduction in computational overhead compared to “whole content” encryption schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".