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SLPA: Single-Line Pixel Attack on Specific Emitter Identification Using Time-Frequency Spectrogram
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DOI：10.1109/TVT.2024.3405547.png)
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摘要
En 中文
Specific emitter identification (SEI) can identify and characterize individual radio frequency emitters, such as radar systems or communication devices. The application of deep learning (DL) techniques in the domain of SEI has gained significant attention due to its potential for enhancing the accuracy and efficiency of SEI. Recent studies show that DL-based SEI is susceptible to adversarial attacks. For SEI based on the time-frequency spectrogram, we propose a single-line pixel attack (SLPA) method. The attacker adds the same perturbation to the time-frequency spectrogram samples line by line and queries the model recognition results, selecting the RF signal corresponding to the line with the highest number of successful attacks as the optimal perturbation signal. This is a practical black-box attack that does not require synchronization and knowledge of model parameters. Under the same perturbation power, our proposed method can achieve an attack success rate of 35.05%, which is much higher than the 20.10% of the white-box attack fast gradient sign method (FGSM) that requires gradient information of the target model.
Keyword:
Time-frequency analysis
Perturbation methods
Spectrogram
Glass box
Closed box
Training
Synchronization
Black-box attack
specific emitter identification (SEI)
time-frequency spectrogram
universal adversarial perturbation
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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