Practical Deep Dispersed Watermarking with Synchronization and Fusion
- URL: http://arxiv.org/abs/2310.14532v1
- Date: Mon, 23 Oct 2023 03:34:05 GMT
- Title: Practical Deep Dispersed Watermarking with Synchronization and Fusion
- Authors: Hengchang Guo, Qilong Zhang, Junwei Luo, Feng Guo, Wenbin Zhang,
Xiaodong Su, Minglei Li
- Abstract summary: We propose a practical deep textbfDispersed textbfWatermarking with textbfSynchronization and textbfFusion.
Our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28% and 5.93% against single and combined attacks, respectively.
- Score: 10.633580224539337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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