Image Encryption Algorithm Based on a New Four-Dimensional Hyper Chaotic System and Second Order Fuzzy Differential Equations
Bibliographic record
Abstract
Given the ongoing need for data security and the ongoing developments that have led to the transmission of sensitive images in various fields, such as the military, forensic image analysis, and medical, the need arose to design this algorithm, which consists of several encryption steps.Design a new hyper-chaotic four-dimensional (4D) system and use it to create a substitution box (S-box) to do substitution and encrypt the color channels by applying different sequence values with XOR for each channel.Also, a second-order fuzzy differential equation is used to generate the final encryption key, which is then XORed with the original image to produce an encrypted color image, thereby enhancing diffusion and confusion.The algorithm showed high accuracy results in examining tests such as entropy value reach 7.999, Histogram, Number of Pixels Change Rate (NPCR), Peak Signal-to-Noise Ratio (PSNR) above than 99.60 percent, Time speed less than 1.07 sec, Unified Average Changing Intensity (UACI) close to 33 percent, Mean Squared Error (MSE), Correlation coefficient test less than (0.001), for the three directions: vertical, horizontal, and diagonal.Confirms the algorithm's ability to withstand brute-force exploration, statistical analysis, and differential cryptanalysis, ensuring a high level of security and preserving the transmitted image from tampering.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".