High-Capacity Steganography Using Object Addition-Based Cover Enhancement for Secure Communication in Networks
Bibliographic record
Abstract
Steganography is an essential way to ensure secure communication in networks. Most steganographic algorithms imperceptibly embed secret information into an existing cover image. However, they generally cannot find a good trade-off between embedding capacity and security, as the existing covers available for users are usually far from optimal for embedding. To address this issue, instead of directly using the existing cover images, we propose a cover enhancement scheme for high-capacity image steganography, in which textured objects are generated and adaptively pasted to an existing cover based on the estimated embedding probability maps. Specifically, by estimating the embedding probability map of the cover image, we locate the high-embedding-cost region (HECR), which is inappropriate for embedding. Then, a textured object is generated by the conditional generative adversarial networks with the input of an affinely transformed object mask, and then is pasted to the located HECR for steganography. Since the image regions inappropriate for embedding are replaced by the textured object regions, the proposed scheme can provide a much higher embedding capacity for the state-of-the-art steganographic approaches. Extensive experiments demonstrate that the proposed scheme provides high embedding capacity,i.e., about 2.5 times higher than the state-of-the-art steganographic methods, and comparable anti-detectability to those methods.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".