Multiple-Image Encryption Using Sine Quadratic Polynomial Mapping and U-Shaped Scanning Techniques
Bibliographic record
Abstract
In the realm of digital image security, the multiple-image encryption (MIE) has garnered increasing attention due to the prevalent dissemination of digital imagery.Responding to this trend, an innovative encryption method has been developed, capable of securing an arbitrary number of images efficiently.This method is underpinned by the newly devised sine quadratic polynomial map (SQPM) and an original space-filling curve technique, termed U-shaped scanning.Extensive analysis, including 2D and 3D phase diagrams, Lyapunov exponents, bifurcation diagrams, and approximate entropy calculations, confirms the SQPM's chaotic properties over a broad spectrum of control parameters.The U-shaped scanning method, novel in its application, facilitates the traversal of every element in a 2D array, irrespective of its dimensions.This method is integral to the permutation phase of the encryption process, where it pre-scrambles input images, and it plays a pivotal role in the diffusion phase through the introduction of U-shaped diffusion.Comprehensive security assessments have been conducted, encompassing secret key analysis, histogram evaluation, correlation assessments, differential analysis, and information entropy measurements.Further scrutiny involves known-plaintext and chosen-plaintext attack resilience, along with visualizations of data loss and noise attack impacts, and execution time analysis across three sets of four images.The results of these security analyses affirm the efficacy of the proposed technique in encrypting multiple images, be they colored or grayscale.This work not only advances the field of image encryption but also introduces novel methodologies with broad applicability in digital image security.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".