  Watermarking of deep neural networks (DNN) can enable their tracing once
released by a data owner. In this paper, we generalize white-box watermarking
algorithms for DNNs, where the data owner needs white-box access to the model
to extract the watermark. White-box watermarking algorithms have the advantage
that they do not impact the accuracy of the watermarked model. We propose
Robust whIte-box GAn watermarking (RIGA), a novel white-box watermarking
algorithm that uses adversarial training. Our extensive experiments demonstrate
that the proposed watermarking algorithm not only does not impact accuracy, but
also significantly improves the covertness and robustness over the current
state-of-art.
