Data hiding and extraction using pseudo-random generation and cover image replication
Bibliographic record
Abstract
This paper introduces a new algorithm in Steganography for concealing secret messages or images within digital images. The algorithm is designed to produce stego images that can be transmitted to recipients without detection by potential attackers, thereby ensuring secure communication channels. The proposed algorithm employs a multi-level randomization technique to embed data within randomly selected cover images, with each byte of the secret image distributed across multiple cover images. This approach contrasts with conventional methods that hide data within a single cover image. Moreover, the algorithm incorporates a load-balancing priority system, a critical feature that ensures uniform stego image quality across the dataset. This strategic approach minimizes variations in Peak-Signal-to-Noise-Ratio (PSNR) values, contributing to consistent performance during data hiding and extraction processes and enhancing communication security. The security and recoverability of the secret image are further improved by a simplified Cipher key system based on SHA-256, which facilitates pseudo-random number generation. This system ensures that the hidden image can be recovered at the receiver's end, even in the face of potential attacks. Experimental results demonstrate comparable PSNR quality to existing methods, particularly when utilizing equal total resolution to deep hiding algorithms. Notably, the proposed algorithm offers an alternative to encryption by leveraging randomization, thereby complicating data extraction for potential attackers by distributing data across multiple images with a randomly generated cipher key.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
New steganography algorithm for hiding data in images; computer security.
The study proposes and evaluates a steganographic data-hiding algorithm.
Steganography algorithm using cover-image replication; security engineering, not research reproducibility.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".