An Enhanced Steganography Approach for Concealing Audio in Images Using Double Density-Dual Tree Wavelet Transform
Bibliographic record
Abstract
Steganography, the art of concealing information within another message or physical object to evade detection, has potential applications across multiple digital content types, including text, photos, videos, and audio.The hidden data size significantly influences the difficulty of detection.Conversely, the data amount that can be concealed within an image is largely dependent on the cover image dimensions, a concept often overlooked by steganographers.Despite numerous attempts to improve embedding capacity, the quality of generated stego-images remains subpar, and embedding capacity continues to be restricted by the cover image size.This study introduces an image steganography approach, leveraging double density dual tree wavelet transform (DDDT-DWT), designed to enhance capacity while preserving optimal quality.The performances of discrete wavelet transform (DWT), double density DWT (DD-DWT), and double density dual tree DWT (DDDT-DWT) are implemented, evaluated, and comparatively assessed.Key performance parameters, such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE), are calculated, guiding the selection of the most efficient methodology.The stego-image quality is also measured using the Structural Similarity Index Metric (SSIM).Experimental results indicate that the proposed DDDT-DWT-based method yields superior imperceptibility for the stego image, with a PSNR of 47.8582 and an SSIM of 0.9945.This advancement in steganography presents opportunities for increasingly undetectable and efficient data concealment.
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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 | 0.001 |
| 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".