Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands

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 subband 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.

I. Introduction

Morphing attack detection is of great significance in high-throughput border control applications. According to the CIA triad model, consisting of three main components, confidentiality, integrity, and availability of secure systems, morphed images violate the integrity of verification systems. A morphed image is generated using genuine face images from two different individuals. Because the resulting morphed image inherits characteristics of both subjects, it can be verified against both real subjects. Morphed images are generated using two approaches. In the first approach [1]- [3], two real face images are alpha blended in order to create a morphed image. To eliminate the ghosting effects in the morphed image, the average of the landmarks in both real images is used as the resulting landmark of the morphed image. In the second approach introduced in [4], a generative model, that is a Generative Adversarial Network (GAN), is trained to synthesize morphed images. Morph detection algorithms can be grouped into two main categories: single and differential morph detection. In the first category, an image under investigation is labeled as morphed or bona fide image, which is known as single image morph detection. In differential morph detection, a subject’s image is compared with a live capture of the subject, and information from both images is used to detect morphed counterfeits. Fig. 1.

A bona fide and a morphed image along with the four corresponding wavelet sub-bands. Using all the bona fide and morphed images in the dataset, 48 pairs of entropy distributions are found for bona fide and morphed images. Given a sub-band, dissimilarity between the two entropy distributions represents how discriminative that sub-band is with respect to morph detection. In the figure, sub-bands 16 and 40 are more discriminative than 6 and 32. A deep classifier is trained using the selected informative sub-bands.