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Authorship verification using deep belief network systems
Author(s) -
Brocardo Marcelo Luiz,
Traore Issa,
Woungang Isaac,
Obaidat Mohammad S.
Publication year - 2017
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3259
Subject(s) - computer science , merge (version control) , bernoulli's principle , gaussian , word error rate , basis (linear algebra) , artificial intelligence , block (permutation group theory) , natural language processing , data mining , theoretical computer science , algorithm , information retrieval , mathematics , physics , geometry , quantum mechanics , engineering , aerospace engineering
Summary This paper explores the use of deep belief networks for authorship verification model applicable for continuous authentication (CA). The proposed approach uses Gaussian units in the visible layer to model real‐valued data on the basis of a Gaussian‐Bernoulli deep belief network. The lexical, syntactic, and application‐specific features are explored, leading to the proposal of a method to merge a pair of features into a single one. The CA is simulated by decomposing an online document into a sequence of short texts over which the CA decisions happen. The experimental evaluation of the proposed method uses block sizes of 140, 280, 500 characters, on the basis of the Twitter and Enron e‐mail corpuses. Promising results are obtained, which consist of an equal error rate varying from 8.21% to 16.73%. Using relatively smaller forgery samples, an equal error rate varying from 5.48% to 12.3% is also obtained for different block sizes.