Dynamical Selective Image Encryption Using Chaos
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
Image data has been increasingly generated by cameras as well as acquisition modalities. Image data is diverse and has different sensitive levels. Encryption algorithms for massive image data are required not only high confidential but also high speed. Sometimes, the trade-off between the speed and confidentiality of an encryption occurs for image data, and it can be obtained with selective image encryption. However, most of existing selective image encryption algorithms are with fixed values of parameters, as a consequence, the confidentiality is threatened from cryptanalysis methods. In this paper, the scheme of selective image encryption using chaos is proposed, in which its parameters are changed dynamically. Specifically, the diffusion is carried out with dynamically selected pixels, and on its varying number of significant bits. The permutation performs on blocks of selected pixels with varying size. Intuitively, the security of the proposed scheme is improved by dynamical selective of data for the encryption. The exemplar simulation shows the effectiveness of the proposed scheme with security analysis by means of testing entropy, and correlation between neighbor pixels. The amount of data to be encrypted is also measured in compared with existing selective image encryption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it