  Intellectual property (IP) protection for Deep Neural Networks (DNNs) has
raised serious concerns in recent years. Most existing works embed watermarks
in the DNN model for IP protection, which need to modify the model and lack of
interpretability. In this paper, for the first time, we propose an
interpretable intellectual property protection method for DNN based on
explainable artificial intelligence. Compared with existing works, the proposed
method does not modify the DNN model, and the decision of the ownership
verification is interpretable. We extract the intrinsic features of the DNN
model by using Deep Taylor Decomposition. Since the intrinsic feature is
composed of unique interpretation of the model's decision, the intrinsic
feature can be regarded as fingerprint of the model. If the fingerprint of a
suspected model is the same as the original model, the suspected model is
considered as a pirated model. Experimental results demonstrate that the
fingerprints can be successfully used to verify the ownership of the model and
the test accuracy of the model is not affected. Furthermore, the proposed
method is robust to fine-tuning attack, pruning attack, watermark overwriting
attack, and adaptive attack.
