Binary Code Optimized for Partial Encryption
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 common technique for the partial encryption of compressed images and videos encrypts only the sign bits of some syntax elements such as the quantized transform coefficients or the motion vector differences. The sign bit can be interpreted as the most significant bit (MSB) in the binary representation of the syntax element. Our work is motivated by the key observation that the binary code used for this representation has an impact on the quality of the reconstruction at the eavesdropper and on the size of the stream to be encrypted. Therefore, we address the problem of optimal binary code design for partial encryption. Ideally, the goal is to simultaneously maximize the eavesdropper's distortion and minimize the length of the compressed MSB stream. Since these two objectives are conflicting in general, we formulate the problem as the maximization of a weighted sum of the eavesdropper's distortion and of the probability of the MSB being 0. We cast the problem as a binary integer linear program equivalent to a maximum weight matching problem, which has a polynomial-time solution algorithm. We show that when the source to be quantized and the quantizer are symmetric, the problem can be converted to a linear program of a smaller size, for a family of distortion metrics. Extensive experiments assess the performance of the optimized binary code in comparison with existing approaches. The results reveal that certain existing partial encryption schemes could benefit from the proposed design.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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