Secure INN-based Steganography via Model Smoothing and Adversarial Attacks
Bibliographic record
Abstract
In recent years, image steganography methods based on invertible neural networks (INNs) have received significant attention due to their invertible structure, which offers advantages in embedding and extracting secret messages. However, current INN-based image steganography methods face challenges, particularly their limited tolerance against noise interference (e.g., added Gaussian noise, adversarial perturbations, and JPEG compression) and vulnerability to detection by advanced deep steganalyzers. To address these concerns, we present a novel steganography framework that combines Median Smoothing Training (MST) with dynamic Projected Gradient Descent (d-PGD). Specifically, our method begins with employing an MST strategy during the training phase to improve the INN’s tolerance to noise, ensuring that accurate message extraction even under noise interference. Subsequently, to improve the security of INN-based steganography, we propose a d-PGD algorithm that can generate minimal adversarial perturbations capable of deceiving deep steganalyzers, thereby improving security without compromising extraction accuracy. Experimental results demonstrate that our method achieves state-of-the-art secret message extraction accuracy while significantly improving resistance against deep steganalyzers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".