Coverless Information Hiding Based on Probability Graph Learning for Secure Communication in IoT Environment
Bibliographic record
Abstract
To securely transmit secret data between Internet of Things (IoT) nodes, it is required to the implement information hiding technique for secure communication in the IoT environment. The traditional information hiding approaches generally select a multimedia file, such as texts, images, and video clips as the cover, and then embed secret information into the cover by slight modification. However, it is not feasible to directly apply these approaches in the IoT environment for the following reasons. First, it is hard for some IoT nodes to effectively and efficiently process and transmit the complex multimedia data. Second, the modification trace left in the cover will cause the presence of hidden secret information to be easily exposed by steganalysis tools. To address the above issues, we propose a coverless information hiding scheme based on probability graph learning for secure communication in the IoT environment. Instead of modifying an existing multimedia cover, we conceal secret information in a generated sequence of IoT data to realize secure communication between different nodes in the IoT environment. According to the node-data interaction relationships, we first learn the transition probability graph (TPG) to describe the transition probabilities between IoT data elements. Then, guided by a given secret message that needs to be hidden, we sequentially select a set of highly correlated data elements from the TPG to generate the sequence. The experimental results and theoretical analysis demonstrate that the proposed information hiding scheme can achieve high hiding capacity with desirable imperceptibility and security performances in the IoT environment.
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 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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".