Secure Data Hiding Technique for Video Steganography
Bibliographic record
Abstract
Multimedia material, such as digital video, is utilized to conceal a secret message.Given the features of digital video, which has a large storage capacity, confidential data may be inserted.Three requirements must satisfy to grantee secure steganography system.These requirements include security, robustness, and imperceptibility.This paper proposes a steganography scheme to enhance the security of video steganography and attempt to meet the three requirements mentioned above.The security requirement is accomplished through two levels.In the first level, the secret message is encrypted before the embedding process using proposed encryption method based on a combination of chaotic and Arnold's map.In the second level, the secret message is embedded in the selected frames of the video.Instead of traditional LSB technique, we will use a modified LSB technique to meet the robustness requirement.A modified LSB technique is satisfied by embedding the secret message in the LSB of cover video in frequency domain after applying the integer wavelet transform (IWT).According to the experimental results, the stego video quality is like the original video where the obtained PSNR value was 61.922, so the third requirement, imperceptibility, was satisfied.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".