Practical Deep Dispersed Watermarking with Synchronization and Fusion
Hengchang Guo Qilong Zhang Junwei Luo Feng Guo Wenbin Zhang Xiaodong Su Minglei Li
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.