New Approach to Image Encryption Based on Large Invertible Pseudo-Random Matrices
Why this work is in the frame
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Bibliographic record
Abstract
In the digital age, where secure image transmission is essential, we present an improved image encryption scheme based on large scalable Hill matrices defined over the Z/256Z ring.The encryption matrix is constructed by multiplying two triangular matrices generated from chaotic maps, providing a high degree of randomness and unpredictability.Each block incorporates arbitrary square submatrices, enhancing the structural complexity of the encryption.Experiments conducted on a diverse set of images validate the robustness of our approach: the correlation between the clear and encrypted images is close to zero, the entropy reaches 7.99 bits per pixel, and the performance achieves an NPCR of 99.64%, a UACI of 33.45%, and an avalanche effect of 50.33%.These results significantly outperform those of traditional variants of the Hill cipher, highlighting the effectiveness of the combination of evolving matrices and chaotic sequences for reliable and efficient image encryption.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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