A feasibility study of watermark embedding in RNN models

Kota Matsumoto; Shigeyuki Sakazawa

doi:10.1117/12.2626104

30 April 2022 A feasibility study of watermark embedding in RNN models

Kota Matsumoto, Shigeyuki Sakazawa

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770N (2022) https://doi.org/10.1117/12.2626104
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Deep learning models are created using a large amount of time and data, and are therefore very costly. Therefore, attention has been focused on technologies that protect rights by embedding digital watermarks in learning models. In this work, we target recurrent neural networks (RNNs) and embed watermarks during model training. There are few studies that show the possibility of watermark embedding for RNN training models. Therefore, in our previous research, we have shown that watermark embedding is possible for learning models generated by LSTM networks, a type of RNN, and have conducted detection. In this paper, we investigate the effect of watermarking on the model when it is embedded into the training model of RNN. In particular, we will conduct experiments and discuss the results regarding the impact of embedding watermarks on the task and the impact of increasing the number of bits embedded in the watermark.

Citation Download Citation

Kota Matsumoto and Shigeyuki Sakazawa "A feasibility study of watermark embedding in RNN models", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770N (30 April 2022); https://doi.org/10.1117/12.2626104

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