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Intra-Class Universal Adversarial Attacks on Deep Learning-Based Modulation Classifiers
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AI
DOI：10.1109/LCOMM.2023.3261423.png)
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Abstract
En 中文
Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical threat model which utilizes adversarial attacks for anti-eavesdropping is proposed and a physical intra-class universal adversarial perturbation (IC-UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, we develop a physical attack algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. Finally, the numerical results corroborate the effectiveness of the proposed approach based on the benchmark dataset.
Keywords:
Receivers
Modulation
Jamming
Wireless communication
Perturbation methods
Synchronization
Transmitters
Adversarial attacks
deep learning
modulation classification
universal adversarial attacks
wireless security
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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