Implementation of Super Encryption Using Affine Cipher, Playfair Cipher, and RSA on Image Files
Bibliographic record
Abstract
This research aims to enhance the security of image files by implementing a super-encryption technique that integrates three cryptographic algorithms from both classical and modern domains: Affine Cipher, Playfair Cipher, and RSA. Each algorithm provides a distinct layer of encryption applied sequentially—starting with byte-value transformation using the Affine Cipher, followed by byte-pair substitution through the Playfair Cipher, and concluding with public-key RSA encryption. The proposed approach was evaluated on image files while ensuring both integrity and byte-level equivalence between the original and decrypted files. The implementation was developed as a desktop application in Visual Basic .NET, featuring separate modules for encryption and decryption, along with structured displays of results and process logs. Experimental results indicate that this super-encryption method successfully preserves file integrity and significantly increases cryptographic complexity without altering file size. System security is substantially improved, as the combined algorithms make the encrypted data highly resistant to analysis without complete knowledge of the underlying structure and encryption keys. This approach offers a viable alternative for securing sensitive image files, such as identity documents and medical records.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".