  Deep convolutional neural networks have made outstanding contributions in
many fields such as computer vision in the past few years and many researchers
published well-trained network for downloading. But recent studies have shown
serious concerns about integrity due to model-reuse attacks and backdoor
attacks. In order to protect these open-source networks, many algorithms have
been proposed such as watermarking. However, these existing algorithms modify
the contents of the network permanently and are not suitable for integrity
authentication. In this paper, we propose a reversible watermarking algorithm
for integrity authentication. Specifically, we present the reversible
watermarking problem of deep convolutional neural networks and utilize the
pruning theory of model compression technology to construct a host sequence
used for embedding watermarking information by histogram shift. As shown in the
experiments, the influence of embedding reversible watermarking on the
classification performance is less than 0.5% and the parameters of the model
can be fully recovered after extracting the watermarking. At the same time, the
integrity of the model can be verified by applying the reversible watermarking:
if the model is modified illegally, the authentication information generated by
original model will be absolutely different from the extracted watermarking
information.
