IMGTXT: Image to Text Encryption Based on Encoding Pixel Contrasts
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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