In recent years, deep neural network (DNN) compression systems have proved to
be highly effective for designing source codes for many natural sources.
However, like many other machine learning systems, these compressors suffer
from vulnerabilities to distribution shifts as well as out-of-distribution
(OOD) data, which reduces their real-world applications. I