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Deep learning has been achieving top performance in many tasks. Sincetraining of a deep learning model requires a great deal of cost, we need totreat neural network models as valuable intellectual properties. One concern insuch a situation is that some malicious user might redistribute the model orprovide a prediction service using the model without permission. One promisingsolution is digital watermarking, to embed a mechanism into the model so thatthe owner of the model can verify the ownership of the model externally. Inthis study, we present a novel attack method against watermark, querymodification, and demonstrate that all of the existing watermark methods arevulnerable to either of query modification or existing attack method (modelmodification). To overcome this vulnerability, we present a novel watermarkingmethod, exponential weighting. We experimentally show that our watermarkingmethod achieves high verification performance of watermark even under amalicious attempt of unauthorized service providers, such as model modificationand query modification, without sacrificing the predictive performance of theneural network model.

Robust Watermarking of Neural Network with Exponential Weighting