Concealing Radio Frequency Fingerprints via Active Adversarial Perturbation
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
Radio frequency fingerprinting (RFF) is an emerging technology to identify a radio device via recognizing its unique feature that originated from manufacturing imperfections. Nevertheless, these invariant hardware characteristics can be exploited by adversaries, potentially compromising device privacy, even with user anonymity measures in place. This paper addresses the challenge of concealing RF fingerprints in the context of pilot signal-based fingerprint identification. Specifically, artificial perturbations are applied to the radio signal, focusing on the pilot signal during radio transmission. To prevent neural models from recognizing the unique hardware-induced features embedded in the pilot signal, we generate a set of active artificial perturbations through adversarial attack optimization. The perturbed signal aims to mislead the neural models in device identification, thereby obfuscating RF fingerprints and preventing unauthorized identification from potential attackers. To ensure normal communication is unaffected, we theoretically analyze the channel estimation error caused by the perturbation to the pilot signal, and show that the impairments to communication can be controlled within a limited range. To demonstrate effectiveness, we implement the entire fingerprinting and de-fingerprinting process based on a 4G LTE testbed. Extensive experiments demonstrate that the proposed method can effectively conceal the device-specific feature against RF fingerprinting neural models.
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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 | 0.000 | 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) | 0.000 | 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