Multi-Task Adversarial Attacks for Wireless Communication Signals

doi:10.1109/TCOMM.2026.3678390

Multi-Task Adversarial Attacks for Wireless Communication Signals

2026-03-27

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DOI：10.1109/TCOMM.2026.3678390

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With the rapid development of deep learning in wireless communication for signal detection, target recognition, and parameter estimation, the vulnerability of models to adversarial examples poses a critical challenge to system robustness and security. Existing adversarial attacks mainly focus on single-task models, limiting their applicability in multi-task scenarios. To address this issue, we propose a Multi-Teacher Distillation-guided Multi-Task Attack (MTDMA) framework. It integrates three high-performance teacher models—direct sequence spread spectrum (DSSS) detection, modulation recognition, and direction-of-arrival (DOA) estimation—to train a unified student model through hybrid knowledge distillation. By fusing soft targets from multiple teachers, the student jointly learns discriminative features of multiple tasks within a shared representation space. Furthermore, we design a Dual-Representation Momentum Attack (DRMA) that generates perturbations in both amplitude and IQ feature spaces with a momentum mechanism, improving attack transferability and stability. Experimental results demonstrate that MTDMA achieves high attack success rates on three task models and transfers effectively to heterogeneous architectures, outperforming FGSM, BIM, PGD, and MIM. This work extends adversarial attacks from single-task to multi-task settings, providing new insights into robustness evaluation and multi-task adversarial sample generation for wireless communication models.

Keywords:

Wireless communication signals

deep learning

multi-teacher knowledge distillation

adversarial attacks

multi-task attack

Multi-Task Adversarial Attacks for Wireless Communication Signals

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