Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems.Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer protocols, and wireless domain constraints.This paper proposes Magmaw, a novel wireless attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel.We further introduce new objectives for adversarial attacks on downstream applications.We adopt the widely-used defenses to verify the resilience of Magmaw.For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system.Experimental results demonstrate that Magmaw causes significant performance degradation even in the presence of strong defense mechanisms.Furthermore, we validate the performance of Magmaw in two case studies: encrypted communication channel and channel modality-based ML model.Our code is available at https://github.com/juc023/Magmaw.