An Analysis of Effectiveness of StegoAppDB and Data Hiding Efficiency of StegHide Image Steganography Tools
Bibliographic record
Abstract
Steganography is a method and technique of concealing existence of communication by embedding secret messages in digital media such as images, videos, electronic books and audio files. Steganographic techniques can be applied in several different ways. On one hand, people can benefit from their use. For example, the steganographic techniques can be used to protect copyright. On the other hand, the techniques can be used for malicious reasons. The steganography can be used to conceal parts of a ransomware attack or for delivering a malicious JavaScript. Steganalysis is a practice and approach to detect coded messages using visual perception, mathematical analysis, or other methods. Detection methods of steganalysis can be divided into two categories: specific steganalysis and universal steganalysis. The specific detection methods deal with the planned steganographic systems (algorithms), while the general detection methods provide recognition irrespective of what the steganographic systems are. This paper primarily focuses on calculating the data hiding efficiency of the steganographic tool Steghide 0.5.1 and StegoAppDB dataset images. This study also analyzes the performance of different embedding techniques and well-known encryption algorithms (AES, DES, 3DES, RC2) with several modes of operation (CBC, CFB, CTR, ECB, OFB) and how they impact the PSNR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".