NDLI: CAPTCHA design based on moving object recognition problem

Content Provider	IEEE Xplore Digital Library
Author	JingSong Cui LiJing Wang JingTing Mei Da Zhang Xia Wang Yang Peng WuZhou Zhang
Copyright Year	2010
Description	Author affiliation: School of Computer, Wuhan University, Wuhan, Hubei, China (JingSong Cui; LiJing Wang; JingTing Mei; Da Zhang; Xia Wang; Yang Peng; WuZhou Zhang)
Abstract	CAPTCHA is a test that can tell humans and computer programs apart automatically. The aim is to allow the server to identify the visitor is a human or a computer, and only provide services to human. It can improve the current server system and user information security. The static plane visual CAPTCHA based on OCR problems with the advantages of implementation and $operation^{[1]}$ become the mainstream of the current CAPTCHA technology application form. However, with a variety of targeted text segmentation technologies merging, such CAPTCHA based on OCR problems is faced with increasing security threats. In this paper, a new CAPTCHA based on the moving object identification and tracking problems is proposed, which is referred to biological motion vision model. An Innovative Single-frame Zero-knowledge rule is also put forward to make the CAPTCHA generation algorithm based on Edge Mutation. An attacker can log on the test service system, only after he solves the moving object recognition problem. Such animation CAPTCHA will be able to resist the attacks of all the static OCR technology, and resist the mainstream of attacks against the moving object detection.
Starting Page	158
Ending Page	162
File Size	145160
Page Count	5
File Format	PDF
ISBN	9781424473847
e-ISBN	9781424473861
DOI	10.1109/ICICIS.2010.5534730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	CAPTCHA Target tracking Merging Humans Network Security Object recognition Optical character recognition software Application software Face detection Automatic testing Information security Resists Moving Object Recognition Single-frame Zero-knowledge Edge Mutation
Content Type	Text
Resource Type	Article

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