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.
翻译:面部识别检查点,例如交通安全管理局(TSA)使用的身份认证技术(CAT),这是一个非三重安全关切。为了克服因变形演示而带来的风险,我们提议了一种基于波盘的变形检测方法,采用端到端的可训练软关注机制。我们基于注意力的深神经网络(DNN)侧重于对变形检测或决定功能最有空间支持的突出区域,例如,运输安全管理局(TSA)使用的身份认证技术(CAT),这是一个非三重安全关切。为了克服因变形演示而出现的风险,我们建议了一种基于波子的变形检测方法,采用基于波子的、可训练的软关注机制。我们基于注意的深层神经网络(DNNN)侧重于主要利益区域(ROI),这些区域对变形检测或决定功能决定功能最有空间,即变形的二等级软模输出。 变形合成合成合成合成程序回顾有助于我们推测ROI作为面标志的区域,特别是基于地标的变形图、LMA、LAN和MARAR的图像估计,我们可以使用一个真实的地图。