Practical Deep Dispersed Watermarking with Synchronization and Fusion

TL;DR

This paper introduces a practical deep dispersed watermarking method that effectively embeds and extracts watermarks from arbitrary-resolution images, demonstrating robustness against various geometric and non-geometric attacks, outperforming existing methods.

Contribution

The authors propose a novel dispersed watermarking scheme with synchronization and fusion that handles arbitrary resolutions and resists complex attacks, advancing deep watermarking techniques.

Findings

Achieves 5.28% and 5.93% higher bit accuracy against single and combined attacks.

Demonstrates better visual quality and less file size increase compared to state-of-the-art.

Effective watermark extraction through synchronization and message fusion modules.

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.