  With the widespread applications of the deep neural network (DNN), how to
covertly transmit the DNN models in public channels brings us the attention,
especially for those trained for secret-learning tasks. In this paper, we
propose deep network steganography for the covert communication of DNN models.
Unlike the existing steganography schemes which focus on the subtle
modification of the cover data to accommodate the secrets, our scheme is
learning task oriented, where the learning task of the secret DNN model (termed
as secret-learning task) is disguised into another ordinary learning task
conducted in a stego DNN model (termed as stego-learning task). To this end, we
propose a gradient-based filter insertion scheme to insert interference filters
into the important positions in the secret DNN model to form a stego DNN model.
These positions are then embedded into the stego DNN model using a key by side
information hiding. Finally, we activate the interference filters by a partial
optimization strategy, such that the generated stego DNN model works on the
stego-learning task. We conduct the experiments on both the intra-task
steganography and inter-task steganography (i.e., the secret and stego-learning
tasks belong to the same and different categories), both of which demonstrate
the effectiveness of our proposed method for covert communication of DNN
models.
