Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking
This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-tran...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2023-04 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Yakushev, Aleksey Markin, Yury Obydenkov, Dmitry Frolov, Alexander Fomin, Stas Akopyan, Manuk Kozachok, Alexander Gaynov, Arthur |
description | This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-transparent image that is almost imperceptible for human eyes. Watermark image is static and stays on the screen all the time thus watermark present on every captured photograph of the screen. The key components of the approach are three neural networks. The first network generates an image with embedded message in a way that this image is almost invisible when displayed on the screen. The other two neural networks are used to retrieve embedded message with high accuracy. Developed method was comprehensively tested on different screen and cameras. Test results showed high efficiency of the proposed approach. |
doi_str_mv | 10.48550/arxiv.2304.12682 |
format | Article |
fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2304_12682</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2806273932</sourcerecordid><originalsourceid>FETCH-LOGICAL-a522-6963e77e07646e7cf57cbf17e53418296fb3c99b997cfba2fb9b2a3462489e263</originalsourceid><addsrcrecordid>eNotj01Lw0AQhhdBsNT-AE8ueN64mf32JlFroSDUgMewGyYltUnqJhH998a2pzm8z8w7DyE3KU-kVYrf-_hTfycguExS0BYuyAyESJmVAFdk0fc7zjloA0qJGcmeurLx8bNutw90g37P8rpB-l5GxJZlvqGbLoz9QCdubLAd6KrxW6QffsB4Xrwml5Xf97g4zznJX57z7JWt35ar7HHNvAJg2mmBxiA3Wmo0ZaVMGarUoBIyteB0FUTpXHBuyoKHKrgAXkgN0joELebk9nT2aFgcYj31_xb_psXRdCLuTsQhdl8j9kOx68bYTj8VYLkGI5wA8QdbAVTX</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2806273932</pqid></control><display><type>article</type><title>Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Yakushev, Aleksey ; Markin, Yury ; Obydenkov, Dmitry ; Frolov, Alexander ; Fomin, Stas ; Akopyan, Manuk ; Kozachok, Alexander ; Gaynov, Arthur</creator><creatorcontrib>Yakushev, Aleksey ; Markin, Yury ; Obydenkov, Dmitry ; Frolov, Alexander ; Fomin, Stas ; Akopyan, Manuk ; Kozachok, Alexander ; Gaynov, Arthur</creatorcontrib><description>This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-transparent image that is almost imperceptible for human eyes. Watermark image is static and stays on the screen all the time thus watermark present on every captured photograph of the screen. The key components of the approach are three neural networks. The first network generates an image with embedded message in a way that this image is almost invisible when displayed on the screen. The other two neural networks are used to retrieve embedded message with high accuracy. Developed method was comprehensively tested on different screen and cameras. Test results showed high efficiency of the proposed approach.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2304.12682</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Cryptography and Security ; Confidential documents ; Messages ; Neural networks ; Watermarking</subject><ispartof>arXiv.org, 2023-04</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by-nc-nd/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,781,882,27907</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2304.12682$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1109/ISPRAS57371.2022.10076265$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Yakushev, Aleksey</creatorcontrib><creatorcontrib>Markin, Yury</creatorcontrib><creatorcontrib>Obydenkov, Dmitry</creatorcontrib><creatorcontrib>Frolov, Alexander</creatorcontrib><creatorcontrib>Fomin, Stas</creatorcontrib><creatorcontrib>Akopyan, Manuk</creatorcontrib><creatorcontrib>Kozachok, Alexander</creatorcontrib><creatorcontrib>Gaynov, Arthur</creatorcontrib><title>Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking</title><title>arXiv.org</title><description>This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-transparent image that is almost imperceptible for human eyes. Watermark image is static and stays on the screen all the time thus watermark present on every captured photograph of the screen. The key components of the approach are three neural networks. The first network generates an image with embedded message in a way that this image is almost invisible when displayed on the screen. The other two neural networks are used to retrieve embedded message with high accuracy. Developed method was comprehensively tested on different screen and cameras. Test results showed high efficiency of the proposed approach.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Cryptography and Security</subject><subject>Confidential documents</subject><subject>Messages</subject><subject>Neural networks</subject><subject>Watermarking</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj01Lw0AQhhdBsNT-AE8ueN64mf32JlFroSDUgMewGyYltUnqJhH998a2pzm8z8w7DyE3KU-kVYrf-_hTfycguExS0BYuyAyESJmVAFdk0fc7zjloA0qJGcmeurLx8bNutw90g37P8rpB-l5GxJZlvqGbLoz9QCdubLAd6KrxW6QffsB4Xrwml5Xf97g4zznJX57z7JWt35ar7HHNvAJg2mmBxiA3Wmo0ZaVMGarUoBIyteB0FUTpXHBuyoKHKrgAXkgN0joELebk9nT2aFgcYj31_xb_psXRdCLuTsQhdl8j9kOx68bYTj8VYLkGI5wA8QdbAVTX</recordid><startdate>20230425</startdate><enddate>20230425</enddate><creator>Yakushev, Aleksey</creator><creator>Markin, Yury</creator><creator>Obydenkov, Dmitry</creator><creator>Frolov, Alexander</creator><creator>Fomin, Stas</creator><creator>Akopyan, Manuk</creator><creator>Kozachok, Alexander</creator><creator>Gaynov, Arthur</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230425</creationdate><title>Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking</title><author>Yakushev, Aleksey ; Markin, Yury ; Obydenkov, Dmitry ; Frolov, Alexander ; Fomin, Stas ; Akopyan, Manuk ; Kozachok, Alexander ; Gaynov, Arthur</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a522-6963e77e07646e7cf57cbf17e53418296fb3c99b997cfba2fb9b2a3462489e263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Cryptography and Security</topic><topic>Confidential documents</topic><topic>Messages</topic><topic>Neural networks</topic><topic>Watermarking</topic><toplevel>online_resources</toplevel><creatorcontrib>Yakushev, Aleksey</creatorcontrib><creatorcontrib>Markin, Yury</creatorcontrib><creatorcontrib>Obydenkov, Dmitry</creatorcontrib><creatorcontrib>Frolov, Alexander</creatorcontrib><creatorcontrib>Fomin, Stas</creatorcontrib><creatorcontrib>Akopyan, Manuk</creatorcontrib><creatorcontrib>Kozachok, Alexander</creatorcontrib><creatorcontrib>Gaynov, Arthur</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yakushev, Aleksey</au><au>Markin, Yury</au><au>Obydenkov, Dmitry</au><au>Frolov, Alexander</au><au>Fomin, Stas</au><au>Akopyan, Manuk</au><au>Kozachok, Alexander</au><au>Gaynov, Arthur</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking</atitle><jtitle>arXiv.org</jtitle><date>2023-04-25</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by applying on the screen a unique identifying watermark as semi-transparent image that is almost imperceptible for human eyes. Watermark image is static and stays on the screen all the time thus watermark present on every captured photograph of the screen. The key components of the approach are three neural networks. The first network generates an image with embedded message in a way that this image is almost invisible when displayed on the screen. The other two neural networks are used to retrieve embedded message with high accuracy. Developed method was comprehensively tested on different screen and cameras. Test results showed high efficiency of the proposed approach.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2304.12682</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2023-04 |
issn | 2331-8422 |
language | eng |
recordid | cdi_arxiv_primary_2304_12682 |
source | arXiv.org; Free E- Journals |
subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Cryptography and Security Confidential documents Messages Neural networks Watermarking |
title | Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T10%3A45%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Docmarking:%20Real-Time%20Screen-Cam%20Robust%20Document%20Image%20Watermarking&rft.jtitle=arXiv.org&rft.au=Yakushev,%20Aleksey&rft.date=2023-04-25&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2304.12682&rft_dat=%3Cproquest_arxiv%3E2806273932%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2806273932&rft_id=info:pmid/&rfr_iscdi=true |