  Deep learning based blind watermarking works have gradually emerged and
achieved impressive performance. However, previous deep watermarking studies
mainly focus on fixed low-resolution images while paying less attention to
arbitrary resolution images, especially widespread high-resolution images
nowadays. Moreover, most works usually demonstrate robustness against typical
non-geometric attacks (\textit{e.g.}, JPEG compression) but ignore common
geometric attacks (\textit{e.g.}, Rotate) and more challenging combined
attacks. To overcome the above limitations, we propose a practical deep
\textbf{D}ispersed \textbf{W}atermarking with \textbf{S}ynchronization and
\textbf{F}usion, called \textbf{\proposed}. Specifically, given an
arbitrary-resolution cover image, we adopt a dispersed embedding scheme which
sparsely and randomly selects several fixed small-size cover blocks to embed a
consistent watermark message by a well-trained encoder. In the extraction
stage, we first design a watermark synchronization module to locate and rectify
the encoded blocks in the noised watermarked image. We then utilize a decoder
to obtain messages embedded in these blocks, and propose a message fusion
strategy based on similarity to make full use of the consistency among
messages, thus determining a reliable message. Extensive experiments conducted
on different datasets convincingly demonstrate the effectiveness of our
proposed {\proposed}. Compared with state-of-the-art approaches, our blind
watermarking can achieve better performance: averagely improve the bit accuracy
by 5.28\% and 5.93\% against single and combined attacks, respectively, and
show less file size increment and better visual quality. Our code is available
at https://github.com/bytedance/DWSF.
