Attention Aware Wavelet-based Detection of Morphed Face Images
Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson,, Nasser M. Nasrabadi

TL;DR
This paper introduces a wavelet-based deep neural network with attention mechanisms for detecting morphed face images, focusing on salient facial regions in spectral space to improve security in face recognition systems.
Contribution
It presents a novel end-to-end trainable attention-based DNN that operates in wavelet space, specifically targeting facial landmarks for improved morph detection accuracy.
Findings
Effective detection on multiple datasets (VISAPP17, LMA, MorGAN)
Attention maps highlight regions critical for distinguishing genuine and morphed images
Ablation study confirms the importance of attention mechanisms in morph detection
Abstract
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…
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Taxonomy
MethodsSoftmax
