Fortifying Secure Semantic Communication: A Next-Generation Defense Framework Against Model Inversion and Adversarial Threats

In semantic communication, model inversion attacks and adversarial attacks pose a major threat to both data privacy and system mission performance. This article adopts an attacker-centric perspective, providing an in-depth analysis of the potential risks associated with these two types of attacks, and proposing an envisioned FSSC (Fortifying Secure Semantic Communication), which is a novel collaborative secure semantic communication defense framework. The FSSC framework employs a dynamic obfuscation encryption strategy that dynamically encrypts data during semantic extraction and feature transmission. This strategy effectively thwarts attackers from reconstructing the data by eavesdropping on the communication channel, thereby significantly bolstering the system's privacy protection capabilities. Moreover, to tackle the challenge of adversarial attacks that could potentially jeopardize image communication tasks, the FSSC framework proposes a prototype adversarial alignment training method. This method seamlessly integrates dynamic perturbations and robust optimization techniques. The proposed framework not only fortifies the security of semantic information, but also optimizes task performance for image-related tasks, such as reconstruction and classification. Experimental results demonstrate that the proposed FSSC framework significantly outperforms existing comparative schemes in terms of privacy protection, attack resilience, and task performance, demonstrating its wide applicability and reliability. These findings underscore the practical value of the proposed secure semantic communication framework in improving the protection, robustness, and performance of privacy of semantic communication systems.