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.
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Rahman, M.E.U., Waseem, M.S. (2020). A Novel Framework for Detection of Morphed Images Using Deep Learning Techniques. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_17
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DOI: https://doi.org/10.1007/978-3-030-41862-5_17
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