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Anti-screenshot watermarking algorithm for archival image based on deep learning model
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Gu, Wei, Chang, Ching-Chun, Bai, Yu, Fan, Yunyuan, Tao, Liang and Li, Li (2023) Anti-screenshot watermarking algorithm for archival image based on deep learning model. Entropy, 25 (2). p. 288. doi:10.3390/e25020288 ISSN 1099-4300.
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Official URL: https://doi.org/10.3390/e25020288
Abstract
Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | C Auxiliary Sciences of History > CD Diplomatics. Archives. Seals > CD921 Archives Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Digital watermarking -- Research, Archival materials -- Digitization , Archives -- Digitization, Archives -- Data processing, Deep learning (Machine learning), Archival materials -- Reproduction | ||||||||||||
| Journal or Publication Title: | Entropy | ||||||||||||
| Publisher: | MDPI | ||||||||||||
| ISSN: | 1099-4300 | ||||||||||||
| Official Date: | 3 February 2023 | ||||||||||||
| Dates: |
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| Volume: | 25 | ||||||||||||
| Number: | 2 | ||||||||||||
| Page Range: | p. 288 | ||||||||||||
| DOI: | 10.3390/e25020288 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
| Date of first compliant deposit: | 26 April 2023 | ||||||||||||
| Date of first compliant Open Access: | 26 April 2023 | ||||||||||||
| RIOXX Funder/Project Grant: |
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