SEDD: Robust Blind Image Watermarking With Single Encoder And Dual Decoders

Hongxia Wang

Abstract:

Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep
neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which
adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel SingleEncoder-Dual-Decoder (SEDD) watermarking architecture to achieve high imperceptibility and strong robustness. Precisely, the single
encoder utilizes normalizing f low to realize watermark embedding, which can effectively fuse the watermark and cover image.
For watermark extraction, we introduce a parallel dual-decoder to improve the imperceptibility and extracting ability. Extensive
experiments demonstrate that better watermark robustness and imperceptibility are obtained by SEDD architecture. Our method
achieves a bit error rate less than 0.1% under most attacks such as JPEG compression, Gaussian blur and crop. Besides, the proposed
method also obtains strong robustness under combined attacks and social platform processing.