Superencryption of BASE 64 Algorithm and ELGAMAL Algorithm on Android Based Image Security
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 current era of globalization, the development of information technology is growing rapidly, there is a possibility that there will be data leaks when the process of exchanging information is carried out, then security becomes a very important aspect which will cause unwanted things, for example manipulation of images in the form of information systems. If this important information falls into the hands of the wrong person, it can be negative and can be detrimental to the image owner. So a security system is designed that functions to protect the data that is sent while maintaining its authenticity and authenticity. Various ways have been developed for data security, one of which is by using cryptography. Cryptography is the science of securing data by using data transformation so that the resulting data cannot be understood by other parties. This transformation can provide a solution to two data security problems, namely the problem of privacy and data authentication. Cryptographic techniques can be used to ensure data security, one of which can be utilized is encryption and description of data or in other words encoding data so that only the person concerned can understand the contents of the data. The proper use of information technology is very important to send private and confidential images to certain parties. These images are still in the form of PNG and JPG extensions, for this reason a security system is needed that can protect images that are transmitted through a communication network, one way that can be done to secure images is using the BASE 64 algorithm and the ELGAMAL algorithm.
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 | 0.001 |
| 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