Physical Adversarial Attacks Against Deep Learning Based Channel Decoding Systems
2020 IEEE Region 10 Symposium (TENSYMP)(2020)
摘要
Deep Learning (DL), in spite of its huge success in many new fields, is extremely vulnerable to adversarial attacks. We demonstrate how an attacker applies physical white-box and black-box adversarial attacks to Channel decoding systems based on DL. We show that these attacks can affect the systems and decrease performance. We uncover that these attacks are more effective than conventional jamming attacks. Additionally, we show that classical decoding schemes are more robust than the deep learning channel decoding systems in the presence of both adversarial and jamming attacks.
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关键词
Adversarial attacks,channel decoding,deep learning,wireless security,neural networks
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