New Approach in Steganography Algorithm by Using Audio and Image as Secure Information Based on Chaotic Method
Bibliographic record
Abstract
In the modern era of digital communication, safeguarding sensitive information has become a critical concern due to increasing cyber threats and unauthorized data breaches.Two foundational techniques widely employed to enhance data security are steganography and cryptography.Cryptography transforms information into an unreadable format, securing it from unauthorized access but often signaling the presence of valuable data.Steganography, on the other hand, ensures that sensitive data is invisible to attackers by hiding the information's mere existence.A better level of security can be attained by combining these two methods.This study presents a new chaotic-based steganography technique in which images are used as the main information-hiding medium.Additionally, for the first time, a novel combination of audio and image as hidden data is suggested.Confidential information is better protected by this two-layered approach, which makes it more resistant to illegal eavesdropping and hacking attempts.Strong security is ensured by the suggested system's use of a high-precision chaotic map for encryption.With a Mean Squared Error (MSE) of 0.083 and a Peak Signal-to-Noise Ratio (PSNR) of 74.87, the simulation results demonstrate the algorithm's efficacy.These measurements attest to the algorithm's capacity to preserve data integrity while offering a high degree of imperceptibility, which qualifies it for real-world use in secure communication.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".