Semantic Camouflage Communications Using Defensive Adversarial Attack: Conceal Truth while Show Fake

This paper introduces defensive adversarial attacks aimed at enhancing the security of semantic communication systems by confusing potential eavesdroppers. Existing research predominantly focuses on enhancing the accuracy of semantic communications while neglecting the security vulnerabilities posed by eavesdroppers. In this study, from the standpoint of physical layer security, defensive adversarial attacks are employed to introduce artificial noise into semantic communications, effectively concealing real information. This artificial noise is generated by deep neural networks to mislead eavesdroppers into perceiving the content of images as unrelated information, with little probability of disrupting normal semantic communications. Experimental results demonstrate that the proposed model can selectively mislead the decoding efforts of eavesdroppers, while ensuring uninterrupted decoding by legitimate receivers.