Enhancing Data Hiding Methods for Improved Cyber Security Through Histogram Shifting Direction Optimization
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
Advancements in information and communication technology have facilitated diverse operational environments, spanning across financial to military sectors.However, these advancements carry an escalation in cybersecurity threats, potentially compromising user privacy and security.Among the various mechanisms introduced to mitigate these threats, data hiding methods stand out.These methods embed covert data within cover data, such as audio and video files, thereby providing an additional layer of security.In this study, we develop upon the existing data hiding techniques, enhancing their capacity to conceal varied sizes of covert data.Our proposed method leverages both right and bottom context pixels for a more nuanced data hiding approach.The effectiveness of this scheme is evaluated by quantifying the quality of the stego data, represented by the Peak Signal to Noise Ratio (PSNR) value.Our initial findings indicate that our method yields superior stego data quality, suggesting its potential to accommodate a larger volume of covert data while preserving the similarity between the cover and stego files.This study thus contributes to more robust and efficient data hiding techniques, bolstering cybersecurity measures in the face of increasing digital threats.
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 | 0.000 | 0.000 |
| 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