Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps

• URL: http://arxiv.org/abs/2105.11095v1
• Date: Mon, 24 May 2021 05:18:33 GMT
• Title: Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps
• Authors: Maedeh Jamali, Nader Karim, Pejman Khadivi, Shahram Shirani, Shadrokh Samavi
• Abstract summary: In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Different image processing attacks are simulated as a network layer to improve the robustness of the model.
• Score: 10.072876983072113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital contents have grown dramatically in recent years, leading to increased attention to copyright. Image watermarking has been considered one of the most popular methods for copyright protection. With the recent advancements in applying deep neural networks in image processing, these networks have also been used in image watermarking. Robustness and imperceptibility are two challenging features of watermarking methods that the trade-off between them should be satisfied. In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Dynamic embedding helps the network to have the lowest effect on the visual quality of the watermarked image. Different image processing attacks are simulated as a network layer to improve the robustness of the model. Our method is a blind watermarking approach that replicates the watermark string to create a matrix of the same size as the input image. Instead of diffusing the watermark data into the input image, we inject the data into the feature space and force the network to do this in regions that increase the robustness against various attacks. Experimental results show the superiority of the proposed method in terms of imperceptibility and robustness compared to the state-of-the-art algorithms.

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