Smart Encryption: A Novel Hybrid Approach to Selectively Encrypt Medical Images
Bibliographic record
Abstract
Selective encryption has emerged as a promising solution for securing medical images while maintaining computational efficiency; however, existing approaches often fall short in robustness and time-sensitive applicability. To address these gaps, this study proposes a hybrid selective encryption framework that combines Advanced Encryption Standard (AES) for regions of interest (ROI) with ChaCha20 for non-ROI regions in three-dimensional NIfTI cardiac MRI volumes from the ACDC dataset. Leveraging ground-truth segmentation masks, the framework targets diagnostically sensitive structures for robust cryptographic protection while accelerating the encryption of non-critical areas. Experimental results, validated by a new theoretical model, reveal a 60–70 percent reduction in encryption time compared to full-volume AES encryption. Security is validated using standard image encryption metrics, including entropy, correlation coefficient, Number of Pixels Change Rate, Unified Average Changing Intensity, Peak Signal-to-Noise Ratio, and Mean Absolute Error, all confirming its resilience to statistical and differential attacks. This approach achieves a practical balance between security and performance, offering adaptability for future integration into clinical systems such as DICOM-based teleradiology and cloud-driven medical imaging workflows.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".