LiDiNet: A Lightweight Deep Invertible Network for Image-in-Image Steganography
Bibliographic record
Abstract
This paper introduces a novel, lightweight deep invertible steganography network (LiDiNet) for image-in-image steganography. Traditional methods, while hiding a secret image within a cover image, often suffer from contour shadows or color distortion, making the secret image easily detectable. Additionally, the superposition of multiple invertible networks may complicate network structures and introduce excessive parameters, making the network training and learning processes difficult. LiDiNet addresses these issues by employing multiple invertible neural networks (INNs) to create a pair of coupled invertible processes for image hiding and recovery. A key innovation is the invertible convolutional layer, which streamlines the affine coupling structure in each INN for improved information fusion. In addition, a series of adaptive coordination spatial-wise attention modules are integrated to enhance the network’s effectiveness in image hiding and recovery, thereby elevating the security of the steganography. LiDiNet’s lightweight structure ensures both high-capacity steganography and robustness against steganalysis. Extensive experiments across various image datasets demonstrate LiDiNet’s superior performance, particularly in visual quality and anti-steganalysis capability, compared to existing 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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".