An Intelligent Steganographic Scheme Using Video Frame Neighborhoods
Bibliographic record
Abstract
This paper describes a method for steganographic security of information based on video.Using videos allows for hiding more secret data.The method is based on dividing the video into an ordered sequence of video frames and analyzing groups of video frames to select pixels in each video file, into which secret bits are embedded.Video frame analysis is performed by determining the difference between adjacent video frames and forming an array of numbers that determines the magnitude of the difference for the code of each pixel.By using a threshold processing, we can identify pixels where more secret bits can be hidden compared to the LSB algorithm.Based on the analysis of adjacent video frames and the application of threshold processing, templates are formed according to which secret information is embedded in the video.Traditional steganographic methods, such as LSB substitution, face challenges related to limited embedding capacity and vulnerability to common signal processing attacks.These drawbacks restrict their effectiveness in practical, high-security data hiding scenarios.To overcome these limitations, we propose an intelligent video steganography technique based on interframe pixel differences and adaptive thresholding.By identifying regions with significant temporal variation, the method selectively embeds multiple secret bits using a threshold-guided LSB approach.Additionally, a dynamic duplication mechanism across color channels is employed to improve redundancy and robustness without compromising visual quality.Experimental results show a notable increase in both embedding capacity and resistance to compression and noise, outperforming traditional LSB-based techniques.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".