数字水印
计算机科学
水印
人工智能
稳健性(进化)
计算机视觉
同步(交流)
编码(集合论)
编码器
深度学习
嵌入
模式识别(心理学)
图像(数学)
频道(广播)
计算机网络
生物化学
化学
集合(抽象数据类型)
基因
程序设计语言
操作系统
作者
Hengchang Guo,Q. Zhang,Jun-Wei Luo,Feng Guo,W.G. Zhang,Xiaodong Su,Minglei Li
标识
DOI:10.1145/3581783.3612015
摘要
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 (e.g., JPEG compression) but ignore common geometric attacks (e.g., Rotate) and more challenging combined attacks. To overcome the above limitations, we propose a practical deep Dispersed Watermarking with Synchronization and Fusion, called DWSF. 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 DWSF. 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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