# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
###### Abstract  
Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms. face morphing; morphing attack detection; deep learning; differential attack detection; single-image attack detection +
Footnote †: journal:  
The severity of the security threat posed by morphing attacks is nowadays prompting countries towards legislative acts with the goal of preventing them [8]. It has also turned Morphing Attack Detection (MAD) into a very active research domain with a plethora of published articles proposing numerous solutions to mitigate the problem [6]. Based on the information available at the time of detection, MAD techniques can be categorized into on-line (or differential), and off-line (single-image, or no-reference). In the on-line scenario (applicable e.g., during authentication at an ABC gate), the detector is presented with a stored (i.e., passport) test image, and also with a live capture of the individual, and has to decide whether the test image has been altered or not, by using also information from the reference live image. On the other hand, in the off-line scenario (applicable e.g., during an eMRTD application process), the detector has to assess the authenticity of the input test image, with no other reference available. As it is the case with the majority of classification/detection problems, morphing attack detection is carried out in a feature space of the input. Based on the tools used for feature extraction, namely, whether the features are automatically learned using DCNNs, or pre-selected (e.g., texture descriptors), a further possible categorization of MAD techniques (also inherent to many classification tasks since the advent and widespread use of DCNNs) is that of \"deep\" vs. \"non-deep\" detection. In general, promising results have been reported on both sides, with the main difference being the requirement for large datasets under the \"deep\" scenario, which is a prerequisite for feature learning using modern DCNNs. However, despite the reported results, the general consensus within the research community is that morphing attack detection remains still an open and extremely challenging issue [9]. This is simply a reflection of the fact that the detection of morphing attacks is in its heart an ill-posed problem.