FakeSafe: Human Level Steganography Techniques by Disinformation Mapping Using Cycle-Consistent Adversarial Network
Bibliographic record
Abstract
Steganography is the task of concealing a message within an overt medium such that the presence of the hidden message is barely detectable. Recently, myriads of works have introduced the inchoate techniques of deep learning to the field of steganography. Nevertheless, existing issues like small payload capacity and image distortion have exceedingly suffocated the steganographic research. In this paper, we propose FakeSafe, a novel cycle-consistent adversarial network proffering human-level steganography. Mapping the confidential information into fake messages, FakeSafe efficaciously precludes the detection of steganalysis algorithms and human eyes. There are three contributions in our work: (i) we construct a multi-step FakeSafe mapping, which significantly impedes the steganalysis models to identify and recover the hidden message; (ii) our steganographic models are robust enough since they are applicable to multifarious data domains, including image and text information; (iii) we introduce a coverless solution to embed the clandestine message within a medium of a specific type in lieu of a dedicated cover. We have conducted experiments using both benchmark and real-world data sets to demonstrate potential applications of FakeSafe, whose open source library is available online at: https://github.com/mikemikezhu/fake-safe.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".