INN-based Robust JPEG Steganography Through Cover Coefficient Selection
Bibliographic record
Abstract
Images transmitted through social networks generally undergo JPEG recompression, which degrades image quality and could disrupt embedded secret messages, making error-free extraction of such messages difficult. In this paper, we propose an INN-based robust JPEG steganography framework through cover coefficient selection, which selects appropriate embedding coefficients based on prior knowledge of JPEG compression and the characteristics of invertible neural networks (INNs). When subjected to JPEG recompression with quality factor (QF)=90 and QF=85 during transmission, the proposed method achieves extraction accuracy as high as 100%. Moreover, we introduce a novel learnable noise layer that incorporates rounding and truncation operations into network training, to mitigate the information loss caused by rounding and to limit pixel values of the stego image within the range of [0-255] as much as possible. When compared with existing INN-based methods, our method reduces runtime by 23% and memory usage by 82.14%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".