De-Mark GAN: Removing Dense Watermark with Generative Adversarial Network
Jinlin Wu, Hailin Shi, Shu Zhang, Zhen Lei, Yang Yang, Stan Ziqing Li · 2018
This paper mainly considers the MeshFace verification problem with dense watermarks. A dense watermark often covers the crucial parts of face photo, thus degenerating the performance in the existing face verification system. The key to solving it is to preserve the ID information while removing the dense watermark. In this paper, we propose an improved GAN model, named De-mark GAN, for MeshFace verification. It consists of one generator and one global-internal discriminator. The generator is an encoderdecoder architecture with a pixel reconstruction loss and a feature loss. It maps a MeshFace photo to a representation vector, and then decodes the vector to a RGB ID photo. The succedent global-internal discriminator integrates a global discriminator and an internal discriminator with a global loss and internal loss, respectively. It can ensure the generated image quality and preserve the the ID information of recovered ID photos. Experimental results show that the verification benefits well from the recovered ID photos with high quality and our proposed De-mark GAN can achieve a competitive result in both image quality and verification.