TITLE:
Detection of Stego-Images in Communication among the Terrorist Boko-Haram Sect in Nigeria
AUTHORS:
Owoeye Kolade, Ajayi Adedoyin Olayinka, Fadugba Sunday, Obayomi Adesoji, Isinkaye Folasade Olubusola
KEYWORDS:
Stego, Staganalysis, Logistic Regression, Boko-Haram Sect
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.3 No.4,
November
24,
2015
ABSTRACT: Nigeria was listed as a part of terrorist states by United States of America as a result of Islamic group (Boko Haram Sect) attacks and other activities in the nation. It has also been discovered that the group employs “steganographic” schemes as a secure means for transmitting their hidden information to each other via Internet and social networks. The group has killed thousands of people since their increased insurgency in July, 2009. These challenges have affected the nation’s foreign policies, political and social economic developments. This research addresses the challenges by employing forensic technique using blind steganalysis approach to detect the presence of the hidden messages in images. Image Quality Metric is employed for extracting the features, and logistic regression is trained as the classifier to predict the stego-images. We show the effectiveness of the method by conducting test and analysis with 319 images varying in size and style. The result shows that the performance of the method is better than other steganalysis methods.