IMGTXT: Image to Text Encryption Based on Encoding Pixel Contrasts
Bibliographic record
Abstract
Nowadays, when data is exchanged over the internet, the security of data is critical in every element of life. Unauthorized network access is possible due to information transmission. As image usage increased in most communications, image privacy became an issue. Image encryption is one of the methods used to protect images online. In this paper, we proposed a new approach called IMGTXT that converts the image to text by coding the pixel values depending on locations then encrypts them by any trust encryption text algorithm, so that this method provides resistance to a variety of attacks such as histogram attacks and brute force attack. The state of the art of this research is the image is represented as a text and there is no relationship between the cipher-image and the plain image. Although this results in a large data volume. The proposed technique builds and testes on various images with different sizes, the recorded results demonstrate the technique’s efficacy and robustness to resist the brute force attack and statistical cryptanalysis of original and encrypted images.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".