  This work investigates the well-known problem of morphing attacks, which has
drawn considerable attention in the biometrics community. Morphed images have
exposed face recognition systems' susceptibility to false acceptance, resulting
in dire consequences, especially for national security applications. To detect
morphing attacks, we propose a method which is based on a discriminative 2D
Discrete Wavelet Transform (2D-DWT). A discriminative wavelet sub-band can
highlight inconsistencies between a real and a morphed image. We observe that
there is a salient discrepancy between the entropy of a given sub-band in a
bona fide image, and the same sub-band's entropy in a morphed sample.
Considering this dissimilarity between these two entropy values, we find the
Kullback-Leibler divergence between the two distributions, namely the entropy
of the bona fide and the corresponding morphed images. The most discriminative
wavelet sub-bands are those with the highest corresponding KL-divergence
values. Accordingly, 22 sub-bands are selected as the most discriminative ones
in terms of morph detection. We show that a Deep Neural Network (DNN) trained
on the 22 discriminative sub-bands can detect morphed samples precisely. Most
importantly, the effectiveness of our algorithm is validated through
experiments on three datasets: VISAPP17, LMA, and MorGAN. We also performed an
ablation study on the sub-band selection.
