Randomized Information Hiding in RGB Images Using Genetic Algorithm and Huffman Coding
Bibliographic record
Abstract
Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information.One of the means of protecting and preserving information is to hide it in a digital medium.The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it.The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns.Secondly, the stage of including data after compressing it by the Huffman method.Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB.The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text.The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses.The role of Huffman reduce data size and thus increase the cover capacity.System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".