  Deep learning based semantic communication(DLSC) systems have shown great
potential of making wireless networks significantly more efficient by only
transmitting the semantics of the data. However, the open nature of wireless
channel and fragileness of neural models cause DLSC systems extremely
vulnerable to various attacks. Traditional wireless physical layer key (PLK),
which relies on reciprocal channel and randomness characteristics between two
legitimate users, holds the promise of securing DLSC. The main challenge lies
in generating secret keys in the static environment with ultra-low/zero rate.
Different from prior efforts that use relays or reconfigurable intelligent
surfaces (RIS) to manipulate wireless channels, this paper proposes a novel
physical layer semantic encryption scheme by exploring the randomness of
bilingual evaluation understudy (BLEU) scores in the field of machine
translation, and additionally presents a novel semantic obfuscation mechanism
to provide further physical layer protections. Specifically, 1) we calculate
the BLEU scores and corresponding weights of the DLSC system. Then, we generate
semantic keys (SKey) by feeding the weighted sum of the scores into a hash
function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is
able to further secure semantic communications with a dynamic dummy data
insertion mechanism. Experiments show the effectiveness of our method,
especially in the static wireless environment.
