Intelligent multi-level regions-of-interest (ROI) document image encryption using an online learning model
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-based document management systems have become increasingly popular for handling documents that contain both text and graphical elements. When such systems are used to store confidential information, document security is a key concern. Conventional encryption techniques used for images fail to provide the level of flexibility required by such document management systems. Newer image encryption techniques provide improved flexibility at the cost of backwards compatibility and ease of use. This paper presents a novel approach to document image encryption using an online learning model. The proposed system provides backwards-compatible document image encryption in regions of interest (ROI) with support for multiple levels of authority. Furthermore, the proposed system is capable of learning from user feedback to improve the ROI selection used during the semi-automatic document encryption process. Experimental results from the encryption of test documents demonstrate the effectiveness of the proposed system.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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