Robust Watermarking of Neural Network with Exponential Weighting
Deep learning has been achieving top levels of performance in many tasks. However, since it is costly to train a deep learning model, neural network models must be treated as valuable intellectual properties. One concern arising from our current situation is that malicious users might redistribute proprietary models or provide prediction services using such models without permission. One promising solution to this problem is digital watermarking, which works by embedding a mechanism into the model so that the model owners can verify their ownership of the model externally. In this study, we present a novel attack method against such watermarks known as query modification and demonstrate that all currently existing watermarking methods are vulnerable to either query modification or other existing attack methods (such as model modification). To overcome these vulnerabilities, we then present a novel watermarking method that we have named exponential weighting and experimentally show that our watermarking method achieves high watermark verification performance even under malicious invalidation processing attempts by unauthorized service providers (such as model modification and query modification) without sacrificing the predictive performance of the neural network model itself.