A Comprehensive Survey of Digital Image Steganography and Steganalysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the realm of digital communications, steganography and steganalysis have become a solution for securely exchanging covert information. This survey initiates with an exploration of the widely used passive-warden scenario model, analyzing its significance, key performance indicators, relevant databases, and clarifying some commonly misunderstood fundamental concepts associated with this model. Subsequently, the paper comprehensively examines the evolution and current state of digital image steganography and ste-ganalysis, highlighting the transition from traditional handcrafted based methods to sophisticated deep learning based techniques developed over the past two decades. It offers thorough descriptions and evaluations of typical methods in both steganography and steganalysis, with a particular emphasis on deep learning-based techniques that have emerged in recent years. Furthermore, the survey identifies significant challenges currently faced in translating theoretical research into practical applications. By integrating these insights, the survey not only charts the historical development and technological advancements in steganography and steganalysis but also establishes a proactive agenda for future research aimed at enhancing security in covert communications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it