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.

翻译：这项工作调查了众所周知的变形攻击问题,这引起了生物鉴别学界的极大关注。 摩擦图像暴露了面部识别系统容易被虚假接受,从而产生可怕的后果, 特别是对国家安全应用而言。 为了检测变形攻击, 我们提议了一种基于2D分辨波列变换(2D-DWT) 歧视性波盘子带的方法。 一个有区别的波盘子带可以突出真实和变形图像之间的不一致之处。 我们观察到, 在真实图像中, 给定的子带的增缩和 相同的子带的变换样本中, 暴露了面部识别系统容易被虚假接受, 从而产生可怕的后果。 考虑到这两种变形攻击的变形, 我们发现Kullback- Lebter两种分布之间的差异, 即真实图像的变形和相应的变形图像的变形。 最有歧视的波带子带子带是具有最高对应的 KL- 调度值。 因此, 22个子带带被选为最具有歧视性的变式的变形检测结果。 我们在22个变式的变式的变式的变式轨道上, 一个深的变式的变式的变式的变式的变式的变式的变式的变式的变式, 。