Implementation of the Gauss-Circle Map for encrypting and embedding simultaneously on digital image and digital text
Bibliographic record
Abstract
Abstract This paper discusses implementation of Gauss-Circle Map (GCM) in cryptography and steganography process simultaneously. Cryptography is used for securing data confidentiality, while steganography is used to protect the existence of data. The objects that used in this thesis are digital text and digital images. This research was conducted by designing algorithms for encryption and embedding simultaneously, as well as extraction and decryption simultaneously then implement it with python programming. Results obtained from the observation shows that GCM had randomness level 100% using NIST test with chosen parameter x 0 (1) = x 0 (2) = 0, α (1) = α (2) = 9, β (1) = β (2) = 0.481, K (1) = K (2) = 1000000, and Ω(1) = Ω(2) = 0.5. Algorithm that have been designed have varying degrees of sensitivity according to different parameters, and high key spaces that reaches 2.6244 × 101269. Encrypted image is uniformly distributed since it passes goodness of fit test. Correlation coefficient values of the stego image are at interval [0.89,1] and very close to correlation coefficient values of the cover image. However, Peak Signal to Noise Ratio (PSNR) of the stego image did not meet standard (above 40 dB). Here, the extracted-decrypted stego image have perfect similarity with the original image.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".