Implementation of Real-Time Adversarial Attacks on DNN-based Modulation Classifier
Bibliographic record
Abstract
In this paper, we provide a hardware implementation for over-the-air (OTA) adversarial attack on a deep neural network (DNN)-based modulation classifiers. Although Automatic modulation classification (AMC) using the DNN-based method outperforms the traditional classification, it has been proven that the machine learning (ML) approaches lack robustness against adversarial attacks. Therefore, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. The case study presented evaluates the adversarial attack performance and its effects on the accuracy of the DNN-classifier OTA using a universal software radio peripheral (USRP) B210. Firstly, we develop an intelligent AMC system using USRPs to classify four digitally modulated signals, namely, BPSK, QPSK, 8PSK, and 16QAM, in real-time. We consider a wireless communication system that consists of three software-defined radios (SDRs), namely, transmitter, receiver, and adversarial attack. While the Rx classifies the received signal, using a DNN-based classifier, the adversarial attack node intends to misclassify the DNN-based classifier by perturbing the input data of with an adversarial example. The developed adversarial node implements the Fast-Gradient Sign method (FGSM) to generate the needed perturbation. The results of the conducted experiment show that the DNN-based classifier achieves 97% accuracy in the absence of an adversarial node. However, after deploying the adversarial attack the classifier accuracy drops to 42%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".