A Novel Framework for Detection of Morphed Images Using Deep Learning Techniques

Mohammed Ehsan Ur Rahman and Md. Sharfuddin Waseem
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Mohammed Ehsan Ur Rahman: Kakatiya Institute of Technology and Science
Md. Sharfuddin Waseem: Kakatiya Institute of Technology and Science, Department of Computer Science and Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 181-197 from Springer

Abstract: Abstract The paper deals with the break-ins that the modern-day biometric verification systems like Automatic Border Control, facial unlocking scheme as in many smartphones, and other photo-ID documents generation and verification systems face. One of the most prominent attacks is the facial morphing attack, wherein the system is fooled by asking it to do facial recognition and matching of a person with a photo which is morphed and has features of two persons overlapped. The proposed framework gives a deep insight into the concept of image morphing and the way to analyze the features and allocate them priorities. The system tries to integrate all the features of image that could possibly have an influence on the face image if morphed with another face image. The paper also presents an account of the advantages and disadvantages as well as the intuition of various approaches of face image morphing detection, especially we take into account the deep learning models that have been used previously and try to tune in the parameters and analyze their complexity in order to try various methods to reduce the overfitting of such models.

Keywords: Automatic border control (ABC); Electronic machine readable travel documents (eMRTD); GNU Image manipulation program v2.8 (GIMP); GIMP animation package (GAP); Commercial off-the-shelf (COTS); ePassport; Image quality analysis (IQA) (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_17

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DOI: 10.1007/978-3-030-41862-5_17

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