  Recently, more and more attention has been focused on the intellectual
property protection of deep neural networks (DNNs), promoting DNN watermarking
to become a hot research topic. Compared with embedding watermarks directly
into DNN parameters, inserting trigger-set watermarks enables us to verify the
ownership without knowing the internal details of the DNN, which is more
suitable for application scenarios. The cost is we have to carefully craft the
trigger samples. Mainstream methods construct the trigger samples by inserting
a noticeable pattern to the clean samples in the spatial domain, which does not
consider sample imperceptibility, sample robustness and model robustness, and
therefore has limited the watermarking performance and the model
generalization. It has motivated the authors in this paper to propose a novel
DNN watermarking method based on Fourier perturbation analysis and frequency
sensitivity clustering. First, we analyze the perturbation impact of different
frequency components of the input sample on the task functionality of the DNN
by applying random perturbation. Then, by K-means clustering, we determine the
frequency components that result in superior watermarking performance for
crafting the trigger samples. Our experiments show that the proposed work not
only maintains the performance of the DNN on its original task, but also
provides better watermarking performance compared with related works.
