Image Encryption and Steganography Method Based on AES Algorithm and Secret Sharing Algorithm
Bibliographic record
Abstract
The development that occurred in information technology and the need to transfer knowledge made sensitive information protection very necessary.The key exchange method is an important method between two sides, the sender side, and the receiver side, especially with the use of the symmetric algorithm, the key exchange method achieves two important principles secrecy and authentication.This article presents a new method for the protection of a secret grayscale image.The proposed work is composed of four phases.The initial phase is the key generation.Then the encryption process will be implemented by using the proposed AES encryption algorithm with multiple S-boxes determined by the number of rounds of the algorithm.The third phase applies secret sharing using the Shamir secret sharing scheme (SSSS).The SSSS will split the encryption key of the encryption algorithm that was generated randomly in the previous phase into several shares that will be distributed over multiple locations.The final phase is steganography, which will embed the secret image into an appropriate cover image using the Least Significant Bit (LSB).The obtained results prove that the secret image is completely restored without any change.The reconstruction of the stego image of quality test results was very good with PSNR 46.165 and MSE 1.58.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".