LFSR state sequence image encryption method based on VHDL language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the modern society of digitalization, integration, intelligence, and networking, while people enjoy the convenience of information technology to their production and lives, information security in the network, as the cornerstone of information communication, becomes more and more important. The research is to establish a new image encryption (IE) method based on LFSR state sequence (SS)s in VHDL language, and the stream cipher of LFSR is studied in detail to induce the idea of LFSR SSs based on VHDL language. The correlation coefficient (CC) of the original image (OI) and encrypted image (EI) pixel points (PP) are analyzed from horizontal direction, vertical direction and diagonal direction, and the results show that the CC of adjacent PP of the OI is large, which approaches 1. However, using the encryption algorithm proposed in this paper, the correlation coefficient of the PP of the EI is -0.0282 in the diagonal direction, and the highest correlation coefficient in the horizontal direction is only 0.0122, which indicates that the adjacent PP of the EI are almost uncorrelated with each other. Therefore, the encryption method can well resist statistical attacks, which illustrates the effectiveness, security, and reliability of this new IE method.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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