Multi-Task Adversarial Attacks for Wireless Communication Signals

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 hi