Compressing DNN-based Image/Video Watermarking Models for Resource-Constrained Devices

DNN(Deep Neural Networks)-based image/video watermarking models have shown outstanding robustness against various distortions. However, because the size of DNN-based watermarking models is typically large, it is not practical to deploy them in resource-constrained devices like laptops and smartphones. In this paper, we propose a two-stage approach based on a synergy of model pruning and knowledge distillation to compress DNN-based watermarking models. First, we propose to evaluate the importance of each module in the model through model pruning techniques and identify the redundant and less essential modules that can be safely removed without significantly degrading the model’s accuracy. Then, based on the importance of each module revealed by the pruning process, we design a compact student model via knowledge distillation during the model distillation phase. The student model retains the essential modules while simplifying or removing the less crucial ones. Through knowledge distillation, the student model can learn the expressive power of the original complex model while achieving a much lower computational cost. We can obtain a tailored student model that maintains the performance level of the original model while being significantly more efficient and suitable for deployment on resource-constrained devices. Extensive experiments demonstrate that this framework provides an effective solution for model compression and efficient model deployment.