Electronic Map Visible Watermark Removal with Conditional Generative Adversarial Networks

Watermarks play an important role in the copyright protection of electronic map tiles. Research on visible watermark removal can help to evaluate the effectiveness of watermarks and improve anti- attack capabilities in an adversarial manner. The existing deep learning-based methods of visible watermark removal have problems such as requiring a large number of training samples, low efficiency, and watermark residual or unrealistic maps generated in results after watermark removal. To address these problems, inspired by the idea of image inpainting, this paper proposes a method based on conditional Generative Adversarial Networks (CGAN) to remove the visible watermarks on electronic maps. The network model mainly consists of a generator and a discriminator. Specifically, the generator adopts a U- Net structure, which includes an encoding stage and a decoding stage. In the encoding stage, multiple convolutional layers are used to obtain multi-scale features of the input watermarked map tile, and jump connections are used in the decoding stage to stitch features and up sample them to generate the watermark-free map tiles. The discriminator uses patch GAN, a full convolutional network model based on region discrimination, to evaluate the authenticity of the generated map tiles. In order to enrich the details and improve the verisimilitude of the generated maps, this paper further adds perceptual loss and L1 loss with the adversarial loss of CGAN. By optimizing the loss function of the real watermark-free map and the generated watermark-free map, an extremely real-like generated watermark-free map can be obtained. The proposed model has been extensively tested on a road map dataset, which was retrieved from domestic and foreign electronic map manufacturers such as Google, Gaode, Baidu, etc. It includes over 3000 watermarked map tiles with different watermark patterns like texts, colorful logos, and both. The results demonstrate that the proposed model in this paper can realize batch removal of visual watermarks on various map tiles such as navigation electronic maps and remote sensing images, and the training speed of the model is 4 times faster than the FCN-based approach. The comparison results by using different combination of loss functions show that the proposed perceptual loss and L1 loss can significantly improve the values of the evaluation metrics of MSE, PSNR, and DSSIM, which explain the similarity of the generated map tiles with the real map tiles. In a word, the approach proposed in this paper is efficient and simple. It can effectively protect the geometric and geographic information in the map tiles after watermark removal and realize batch removal without manual interferences. © 2023 Journal of Geo-Information Science. All rights reserved.