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http://dx.doi.org/10.5909/JBE.2020.25.2.166

Deep Learning Framework for Watermark-Adaptive and Resolution-Adaptive Image Watermarking  

Lee, Jae-Eun (Department of Electronic Materials Engineering, Kwangwoon University)
Seo, Young-Ho (Department of Electronic Materials Engineering, Kwangwoon University)
Kim, Dong-Wook (Department of Electronic Materials Engineering, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.25, no.2, 2020 , pp. 166-175 More about this Journal
Abstract
Recently, application fields for processing and using digital image contents in various forms and types are rapidly increasing. Since image content is high value-added content, the intellectual property rights of this content must be protected in order to activate the production and use of the digital image content. In this paper, we propose a deep learning based watermark embedding and extraction network. The proposed method is to maximize the robustness of the watermark against malicious/non-malicious attacks while preserving the invisibility of the host image. This network consists of a preprocessing network that changes the watermark to have the same resolution as the host image, a watermark embedding network that embeds watermark data while maintaining the resolution of the host image by three-dimensionally concatenating the changed host image and the watermark information, and a watermark extraction network that reduces the resolution and extracts watermarks. This network verifies the invisibility and robustness of the proposed method by experimenting with various pixel value change attacks and geometric attacks against various watermark data and host images with various resolutions, and shows that this method is universal and practical.
Keywords
convolutional neural network; deep learning; robust blind watermarking; invisibility; watermark-adaptive; resolution-adaptive;
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