Covert Communication Based on the Poisoning Attack in Federated Learning
Junchuan Liang Rong Wang
Covert communication has become an important area of research in computer
security. It involves hiding specific information on a carrier for message
transmission and is often used to transmit private data, military secrets, and
even malware. In deep learning, many methods have been developed for hiding
information in models to achieve covert communication. However, these methods
are not applicable to federated learning, where model aggregation invalidates
the exact information embedded in the model by the client. To address this
problem, we propose a novel method for covert communication in federated
learning based on the poisoning attack. Our approach achieves 100% accuracy in
covert message transmission between two clients and is shown to be both
stealthy and robust through extensive experiments. However, existing defense
methods are limited in their effectiveness against our attack scheme,
highlighting the urgent need for new protection methods to be developed. Our
study emphasizes the necessity of research in covert communication and serves
as a foundation for future research in federated learning attacks and defenses.