Secure Image Steganography Algorithm Using Radial Basis Function Neural Network
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
Recently, ensuring the security of secret messages over computer networks has significantly increased in importance. For this reason, a new system is proposed that tries to hide text using Artificial Neural Network (ANN), and more precisely using Radial Based Function, with zero mean square error, in addition to encryption techniques, to make sure that the resulting text is exactly the same as the one that was sent. In this study the text is encrypted by an ordinary encryption algorithm, then the encrypted text will be embedded within the image and the positions of each encrypted text value will be determined, and in the last step the taken values (positions) will be encrypted using the neural network. The resulting encrypted text is unpredictable, making it very secure. On the receiver side, only the person, who has knowledge of the decryption key, neural network inputs P and parameters, will be able to see the original message embedded in the image.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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