Paper Info
Title
DeepCAPTCHA: an image CAPTCHA based on depth perception
Abstract
Over the past decade, text-based CAPTCHA (TBC) have become popular in preventing adversarial attacks and spam in many websites and applications including emails services, social platforms, web-based market places, and recommendation systems. However, in addition to several problems with TBC, it has become increasingly difficult to solve in recent years, to keep up with OCR technologies. Image-based CAPTCHA (IBC), on the other hand, is a relatively new concept that promises to overcome key limitations of TBC. In this paper we present an innovative IBC, DeepCAPTCHA, based on design guidelines, psychological theory and empirical experiments. DeepCAPTCHA exploits the human ability of depth preception. In our IBC users should arrange 3D objects in terms of size (or depth). In our framework for DeepCAPTCHA, we automatically mine 3D models, and use a human-machine Merge Sort algorithm to order these unknown objects. We then create new appearances for these objects at multiplication factor of 200, and present these new images to the end-users for sorting (as CAPTCHA tasks). Humans are able to apply their rapid and reliable object recognition and comparison (arise from years experience with the physical environment) to solve DeepCAPTCHA, while machines are still unable to complete these tasks. Experimental results show that humans can solve DeepCAPTCHA with a high accuracy (~84%) and ease, while machines perform dismally.
Year
DOI
Venue
2014
10.1145/2557642.2557653
MMSys
Keywords
DocType
Citations 
new image,image-based captcha,text-based captcha,new concept,depth perception,years experience,new appearance,ibc user,captcha task,innovative ibc,depth preception,broadcasting,optimization
Conference
2
PageRank 
References 
Authors
0.37
25
4
Name
Order
Citations
PageRank
Hossein Nejati1325.29
Ngai-Man Cheung275067.36
Ricardo Sosa332.07
Dawn C. I. Koh420.37