  Distributed deep neural networks (DNNs) have emerged as a key technique to
reduce communication overhead without sacrificing performance in edge computing
systems. Recently, entropy coding has been introduced to further reduce the
communication overhead. The key idea is to train the distributed DNN jointly
with an entropy model, which is used as side information during inference time
to adaptively encode latent representations into bit streams with variable
length. To the best of our knowledge, the resilience of entropy models is yet
to be investigated. As such, in this paper we formulate and investigate the
resilience of entropy models to intentional interference (e.g., adversarial
attacks) and unintentional interference (e.g., weather changes and motion
blur). Through an extensive experimental campaign with 3 different DNN
architectures, 2 entropy models and 4 rate-distortion trade-off factors, we
demonstrate that the entropy attacks can increase the communication overhead by
up to 95%. By separating compression features in frequency and spatial domain,
we propose a new defense mechanism that can reduce the transmission overhead of
the attacked input by about 9% compared to unperturbed data, with only about 2%
accuracy loss. Importantly, the proposed defense mechanism is a standalone
approach which can be applied in conjunction with approaches such as
adversarial training to further improve robustness. Code will be shared for
reproducibility.
