Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 41-44.

• Review • Previous Articles     Next Articles

Overview of Preventing Candid Photos Methods for Electronic Screens

WANG Xiao-yuan, ZHANG Wen-tao   

  1. China Aerospace Academy of Systems Science and Engineering,Beijing 100037,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Nowadays,the performance of mobile phones and other devices has become more and more powerful.It brings convenience and entertainment to people’s life.At the same time,it also reduces the cost of crime to steal business secrets even national secrets.The convenient and covert ways of stealing secrets have brought great challenges to information security.According to existing academic research and business solutions,this paper introduced three kinds of methods:hiding display information,detection camera and screen watermarking.Then it analyzed the characteristics,advantages and limitations of various methods from the angle of information security protection.Finally,a new solution based on computer vision was proposed to avoid the limitations.

Key words: Computer vision, Detection camera, Hiding display information, Preventing candid photos methods, Screen watermark

CLC Number: 

  • TP30
[1]朱杰.美国NSA全球监听和网络窃密行径深度揭秘 看山姆大叔如何玩转“全球监听”[J].中国信息安全,2014(6):80-87.
[2]Risk Based Security.Data Breach Quick-View:An Executice’s Guide to 2013 Data Breach Trends[OL].https://pages.riskbasedsecurity.com/hubfs/Reports/2016_MidYear_DataBreachQuickViewReport.pdf.
[3]范荣.从黄宇间谍窃密案谈加强保密管理对策[J].保密工作,2016(5):40-42.
[4]赵飞.智能手机泄密风险分析及安全保密技术方案[J].电子技术与软件工程,2017(2):216.
[5]ROESSLER M.How to find hidden cameras[OL].http://www.doc88.com/p-9641502582457.html.
[6]TRUONG K N,PATEL S N,SUMMET J W,et al.Preventing camera recording by designing a capture-resistant environment[C]∥Proceedings of the 7th International Conference on Ubiquitous Computing.Tokyo,Berlin-Heidelberg:Springer,2005.
[7]GROSGES T.Retro-reflection of glass beads for traffic road stripe paints[J].Optical Materials,2008,30(10):1549-1554.
[8]YAMADA T,GOHSHI S,ECHIZEN I.Countermeasure of reshooting prevention against attack with infrared-cut filter[C]∥Proc.of IPSJ Symposium on Computer Security(CSS).2010.
[9]MAHDAVI M,MAHDAVI H,FARSI F.System and method for video recording device detection:US,20120128330 [EB/OL].(2012-05-24) [2012-11-15].http://www.faqs.org/patents/app/20120128330.
[10]FUJIKAWA M,AKIMOTO J,ODA F,et al.Study of Countermeasures for Content Leaks by Video Recording[C]∥2011 Sixth International Conference on Availability,Reliability and Security.2011.
[11]GURI M,HASSON O,KEDMA G,et al.An Optical Covert-Channel to Leak Data through an Air-Gap[OL].https://arxiv.org/pdf/1607.03946.pdf.
[12]HUANG H C,FANG W C.Metadata-based image watermar-king for copyright protection[J].Simulation Modeling Practice and Theory,2010,18(4):436-445.
[13]JUNG E H,CHO S Y.A robust digital watermarking system adopting 2d barcode against digital piracy on p2p network[J].IJCSNS International Journal of Computer Science and Network Security,2006,6(10):263.
[14]PIEC M,RAUBER A.Real-Time Screen Watermarking Using Overlaying Layer[C]∥Ninth International Conference on Availability.2014.
[15]耿振民,王衍江.一种防止对屏幕拍照的反泄密方法:CN 103390141 A[P].2013.
[16]张文豪,吴怀宇.基于摄像头检测的防盗拍系统开发和算法研究[J].电子设计工程,2013,21(18):48-52.
[17]汪嘉恒,程雨诗,徐文渊.基于辐射特征的隐藏摄像头检测技术[J].工业控制计算机,2017,30(2):50-52.
[18]汪嘉恒.面向防拍摄的摄像检测技术[D].杭州:浙江大学,2017.
[19]褚晶辉,田叶,苏育挺.基于频率约束的相机与屏幕通信隐写算法[J].激光与光电子学进展,2018,55:051003.
[20]四种屏幕防拍照、截屏、打印等数据泄露水印解决方案[OL].https://www.leagsoft.com/doc/article/1416.html.
[21]防范屏幕拍照泄密不再束手无策[OL].http://www.ip-guard.net/blog/?p=1922.
[22]吴海涛,詹永照.数字水印技术综述[J].软件导刊,2015,14(8):45-49.
[23]吴亚坤,邸春红.数字水印技术综述[J].辽宁大学学报(自然科学版),2010,37(3):202-206.
[24]马颖颖.数字水印攻击方法的一些研究[D].杭州:杭州电子科技大学,2011.
[25]李彦冬.基于卷积神经网络的计算机视觉关键技术研究[D].成都:电子科技大学,2017.
[26]杨益平,闵啸.基于计算机视觉的手势识别人机交互技术[J].电子技术与软件工程,2018(12):138-139.
[1] ZHANG Ji-kai, LI Qi, WANG Yue-ming, LYU Xiao-qi. Survey of 3D Gesture Tracking Algorithms Based on Monocular RGB Images [J]. Computer Science, 2022, 49(4): 174-187.
[2] TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[3] GAN Chuang, WU Gui-xing, ZHAN Qing-yuan, WANG Peng-kun, PENG Zhi-lei. Multi-scale Gated Graph Convolutional Network for Skeleton-based Action Recognition [J]. Computer Science, 2022, 49(1): 181-186.
[4] FENG Fu-rong, ZHANG Zhao-gong. Recent Advances for Object Contour Detection Technology [J]. Computer Science, 2021, 48(6A): 1-9.
[5] ZHANG Kai-hua, FAN Jia-qing, LIU Qing-shan. Advances on Visual Object Tracking in Past Decade [J]. Computer Science, 2021, 48(3): 40-49.
[6] LI Ya-ze, LIU Hong-zhe. Object Detection Based on Neighbour Feature Fusion [J]. Computer Science, 2021, 48(12): 264-268.
[7] CHENG Ming, MA Pei, HE Ru-han. Clothing Image Sets Classification Based on Manifold Structure Neural Network [J]. Computer Science, 2021, 48(11A): 391-395.
[8] CHEN Hao-nan, LEI Yin-jie, WANG Hao. Lightweight Lane Detection Model Based on Row-column Decoupled Sampling [J]. Computer Science, 2021, 48(11A): 416-419.
[9] XIE Hai-ping, LI Gao-yuan, YANG Hai-tao, ZHAO Hong-li. Classification Research of Remote Sensing Image Based on Super Resolution Reconstruction [J]. Computer Science, 2021, 48(11A): 424-428.
[10] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[11] LI Ze-wen, LI Zi-ming, FEI Tian-lu, WANG Rui-lin and XIE Zai-peng. Face Image Restoration Based on Residual Generative Adversarial Network [J]. Computer Science, 2020, 47(6A): 230-236.
[12] ZHANG Peng, SONG Yi-fan, ZONG Li-bo, LIU Li-bo. Advances in 3D Object Detection:A Brief Survey [J]. Computer Science, 2020, 47(4): 94-102.
[13] MIAO Yi, ZHAO Zeng-shun, YANG Yu-lu, XU Ning, YANG Hao-ran, SUN Qian. Survey of Image Captioning Methods [J]. Computer Science, 2020, 47(12): 149-160.
[14] LI Huang, WANG Xiao-li, XIANG Xin-guang. Scene Text Detection Based on Triple Segmentation [J]. Computer Science, 2020, 47(11): 142-147.
[15] LIU Jian, JIN Ze-qun. Facial Expression Transfer Method Based on Deep Learning [J]. Computer Science, 2019, 46(6A): 250-253.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!