This paper introduces a new method for automatically predicting the response time of the Internet users to solve the CAPTCHA test by a regression tree strategy. The input to the model is a set of demographic features of the user: (i) age, (ii) education level, and (iii) Internet experience level. The experiment is performed on 114 Internet users who are invited to solve three image and interactive CAPTCHA tests: (i) FunCAPTCHA, (ii) home numbers, and (iii) picture of the CAPTCHA. Collected demographic features and response time for each user are processed by the regression tree approach in order to evaluate the prediction accuracy. Obtained results revealed the ability of the model in correctly predicting the response time of the specific CAPTCHA types. This represents an invaluable analysis in the state-of-the-art for designing new CAPTCHA types which are more accustomed to specific categories of Internet users.

Usability analysis of the image and interactive CAPTCHA via prediction of the response time

Amelio A.;
2017-01-01

Abstract

This paper introduces a new method for automatically predicting the response time of the Internet users to solve the CAPTCHA test by a regression tree strategy. The input to the model is a set of demographic features of the user: (i) age, (ii) education level, and (iii) Internet experience level. The experiment is performed on 114 Internet users who are invited to solve three image and interactive CAPTCHA tests: (i) FunCAPTCHA, (ii) home numbers, and (iii) picture of the CAPTCHA. Collected demographic features and response time for each user are processed by the regression tree approach in order to evaluate the prediction accuracy. Obtained results revealed the ability of the model in correctly predicting the response time of the specific CAPTCHA types. This represents an invaluable analysis in the state-of-the-art for designing new CAPTCHA types which are more accustomed to specific categories of Internet users.
2017
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Inglese
11th Multi-disciplinary International Workshop on Artificial Intelligence, MIWAI 2017
2017
brn
10607
252
265
14
978-3-319-69455-9
978-3-319-69456-6
Springer Verlag
Artificial intelligence; CAPTCHA; Prediction; Regression tree
none
Brodic, D.; Amelio, A.; Ahmad, N.; Shahzad, S. K.
273
info:eu-repo/semantics/conferenceObject
4
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/770252
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