Implementation of Super Encryption Using Affine Cipher, Playfair Cipher, and RSA on Image Files
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
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.
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 | 0.001 | 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) | 0.000 | 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