  Morphed images have exploited loopholes in the face recognition checkpoints,
e.g., Credential Authentication Technology (CAT), used by Transportation
Security Administration (TSA), which is a non-trivial security concern. To
overcome the risks incurred due to morphed presentations, we propose a
wavelet-based morph detection methodology which adopts an end-to-end trainable
soft attention mechanism . Our attention-based deep neural network (DNN)
focuses on the salient Regions of Interest (ROI) which have the most spatial
support for morph detector decision function, i.e, morph class binary softmax
output. A retrospective of morph synthesizing procedure aids us to speculate
the ROI as regions around facial landmarks , particularly for the case of
landmark-based morphing techniques. Moreover, our attention-based DNN is
adapted to the wavelet space, where inputs of the network are coarse-to-fine
spectral representations, 48 stacked wavelet sub-bands to be exact. We evaluate
performance of the proposed framework using three datasets, VISAPP17, LMA, and
MorGAN. In addition, as attention maps can be a robust indicator whether a
probe image under investigation is genuine or counterfeit, we analyze the
estimated attention maps for both a bona fide image and its corresponding
morphed image. Finally, we present an ablation study on the efficacy of
utilizing attention mechanism for the sake of morph detection.
