Efficient Covert Communication With Ambient OFDM WiFi Backscatter
Bibliographic record
Abstract
Information security is a non-negligible issue for wireless transmission. Covert communication provides high security by concealing the transmitted signals within environmental noise. However, existing solutions suffer from low transmission efficiency. Ambient backscatter, concealing data within ubiquitous ambient signals, provides a promising way to achieve high-efficiency covert communication. In this paper, we propose CoScatter, an efficient covert transmission system based on OFDM WiFi backscatter. Current studies rely on redundant modulation, resulting in low throughput. This paper is to increase throughput and shorten transmission time, thereby reducing exposure risk. This is the first work to realize single-sample level demodulation, efficiently eliminating the redundancy, increasing the throughput, and reducing the transmission time. We discover that the main obstacles are the additional phase offsets introduced by three independent wireless channels in backscatter systems. Based on this, we design a new backscatter channel equalization procedure to remove the channel influences while preserving all the covert information embedded by the tag, realizing an efficient covert transmission. Evaluation results show that Coscatter achieves a throughput exceeding 15.7 Mbps, which is around 64x of that of RapidRider, and 16x of that of Tscatter. Consequently, the exposure risk of CoScatter is reduced to 1/64 of that of RapidRider and 1/16 of that of Tscatter.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".