Image Steganography with Security Using Massive Threefold Attentional Residual GAN Optimized By Chaotic PSO Algorithm
Bibliographic record
Abstract
The purpose of this study is to address the persistent challenges in image steganography, namely suboptimal feature learning, mode collapse, and training instability, which limit the performance of existing CNN- and GAN-based approaches for secure communication. To overcome these issues, a novel framework called Massive Threefold Attentional Residual GAN (MTARGAN) is proposed, in which the GAN hyperparameters are dynamically optimized using a Chaotic Particle Swarm Optimization (CPSO) algorithm. This design enhances feature extraction, embedding efficiency, and robustness against steganalysis. Experimental evaluations demonstrate that the proposed model achieves superior imperceptibility and resilience compared to state-of-the-art methods, with average PSNR values of 36.06, 34.43, 30.05, and 33.92 dB and corresponding SSIM scores of 0.96, 0.86, 0.89, and 0.84 at embedding capacities of 1, 2, 3, and 4 bpp, respectively. These results highlight the model’s ability to maintain a balance between embedding capacity and image quality while ensuring high recovery accuracy and security. Overall, the findings suggest that MTARGAN with CPSO optimization offers a stable, robust, and secure solution for practical image steganography applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".