Chaos Based Pseudo Random Bit Generator Design and Its Application in Secure Image Encryption
Bibliographic record
Abstract
Security and privacy problems in communication systems and social media platforms where digital image/video are shared almost always, have attracted researchers interest on information security. Starting from this point of view, a novel one-dimensional chaotic maps based pseudo random bit generator is proposed to make a significant contribution to the literature about protection of personal data in any network. Electronic circuit realizations of Logistic and Tent maps as entropy source are designed on Orcad-Pspice environment and state variables are inputted to novel post-processor algorithm. The rest of main blocks of proposed pseudo random bit generator design are built up fixed-point binary conversion algorithm, XOR processor and H function post-processor. The generated pseudo random bit series are tested by using NIST 800.22 statistical test suite and applied to color image encryption in order to show the effectiveness of proposed design. Cryptanalysis processes such as histogram, NPCR-UACI and correlation analysis are demonstrated. Analysis results show that the proposed design can be used successfully in many secure communication and media transmission applications.
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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 | 0.000 |
| 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".