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Tolerance of CNN watermarking against model optimizations

Models created by companies and individuals by training a large amount of data are important assets and need to be protected by copyright. As a method of copyright protection, experiments have been conducted to embed a watermark into the learning model. This watermark is embedded directly into the parameters of the learning model, but the values of the parameters will change when the learning model is subjected to model compression process such as quantization. In our previous study, we showed that the effect of quantization on the watermark was small and that the embedded watermark could be retrieved. In this paper, as a further investigation, we conduct experiments on the effect of both pruning and quantization, and quantization aware training on the watermarking when creating the trained model. In the experiments, we used models of two different scales, one large and one small, and performed the above-mentioned processing on each model to check the state of the watermark. The results show that the models with both pruning and quantization show significant degradation of the watermark for small-scale models, but this is eliminated when the models are quantized. In the case of quantization aware training, there was no effect on watermarking.