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Robust Watermarking of Screen-Photography Based on JND

Siyu Gu1, Jin Han1,*, Xingming Sun1,2, Yi Cao1,3

1 Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, 201144, China
3 Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada

* Corresponding Author: Jin Han. Email: email

Computers, Materials & Continua 2022, 71(3), 4819-4833. https://doi.org/10.32604/cmc.2022.023955

Abstract

With the popularity of smartphones, it is often easy to maliciously leak important information by taking pictures of the phone. Robust watermarking that can resist screen photography can achieve the protection of information. Since the screen photo process can cause some irreversible distortion, the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high. Therefore, this paper proposes a new screen-photography robust watermark. In terms of embedding region selection, the intensity-based Scale-invariant feature transform (SIFT) algorithm used for the construction of feature regions based on the density of feature points, which can make it more focused on the key content of the image; in terms of embedding strength, the Just noticeable difference (JND) model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility; after embedding watermark, the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark extraction. After experiments, it is shown that the method we proposed can improve the correct rate of watermark extraction, the quality of the visual aspect of the watermarked picture is also improved.

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Cite This Article

S. Gu, J. Han, X. Sun and Y. Cao, "Robust watermarking of screen-photography based on jnd," Computers, Materials & Continua, vol. 71, no.3, pp. 4819–4833, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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