Compressed 3D Image Information and Communication Security
Bibliographic record
Abstract
Abstract Ensuring information and communication security in military messages, government instructions, scientific experiments, as well as in personal data processing, is critical. In this study, a new hybrid classical–quantum cryptographic scheme to protect image information and communication security is developed by combining a quantum key distribution (QKD) and compressed sensing (CS). This method employs a QKD system to generate true random codes among the remote legitimate users and utilizes these random codes to encrypt and decrypt compressed 3D image information based on the CS algorithm. Therefore, this new technique can provide computational security in the image information transmission process by the encryption and decryption of CS algorithm, and the information and communication security can be evaluated in real time by monitoring the QKD system. Furthermore, this technique can directly transmit and reconstruct the compressed 3D image information based on the modified TwIST algorithm, and thus fewer random codes are required in QKD system, which can improve the information transmission bandwidth. Consequently, this technique not only provides a new application of a QKD system but also extends the CS‐based image reconstruction from 2D to 3D. This study may open a new opportunity in the field of information and security communication.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".