Image Encryption Based on Hybrid Parallel Algorithm: DES-Present Using 2D-Chaotic System
Bibliographic record
Abstract
Image encryption algorithms have recently been developed to protect data from hackers and give recipients privacy. DES is a widely recognized block cypher that has certain vulnerabilities that make it susceptible to differential attacks. The present is a lightweight symmetric algorithm that provides privacy for transferring information over the network but has some drawbacks in that it is difficult to maintain an appropriate level of complexity. The study suggests that to encrypt and decrypt images as quickly as possible, the system uses parallel environments in algorithms (Present and DES). It also uses a 2D-Chaotic key generation system to make the system stronger against statistical, differential, and brute force attacks. Where the DES algorithm uses four rounds, within each one round from the des, the present algorithm executes only four rounds, and the same 2D-Chaotic System is used to generate the key. The keys and blocks are distributed to 4 cores, 5 cores, or 6 cores at the same time. The performance evaluation of the proposed algorithm is quantified by several metrics: All peak signal-to-noise ratio (PSNR) values are low, which means the quality image encryption is good. Unlike MSE, all the values are very high, which indicates that the image we have encrypted has no similarity to the encrypted image. The NPCR value of 99.6658% indicates a high degree of accuracy in changing pixel values. Additionally, a unified average changing intensity (UACI) that doesn't go over 30.90% shows that the algorithm is good at making big changes in pixel intensities. And the analysis speed of the proposed system based on the parallelism of the environment is faster than the sequence algorithms (DES-Present). The results demonstrate the algorithm's ability to encrypt color images, making it useful in applications that require strong data and 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".