# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Methods of Morph Attack Detection
#### 2.4.1 Image Demorphing  
In image demorphing methodology, both captured images and potential morphed images are used to uncover the true identity of the owner of the document. The demorphing model performs mathematical transformations on the potentially morphed and still live images to perform its task. The process of demorphing states that the captured image of the traveler can be linked to the potentially morphed image on the travel document and processing can be done to reveal the true identity of the authentic owner of the travel document. The process of demorphing involves the inversion of the morphing process. This approach proposes that the basic concept of morphed images is assumed to be a linear combination of the accomplice's (authentic owner of travel documents) and the criminal's images, where a criminal's image is the captured picture at the border control and the accomplice's image is the image of the person used to create a morph image by combining it with the criminal's image. The demorphed image is found by removing the live captured image from the potentially morphed image presented on the travel document. Suppose a demorphed image comparison with the live captured image produces a low score. In that case, it means that the presented image in the travel document is a morphed image instead of an authentic image of the traveller. The basic approach of a demorphing-based morph attack detection model is illustrated in Figure 3.  
FD-GAN (Face Demorphing Generative Adversarial Network for Restoring's Facial Image) was used in a previous study [1] to restore the facial image of the accomplice from a morphed image to facilitate the process of accurate morph attack detection. The FD-GAN utilizes the morphed image and the captured picture of the criminal to restore the accomplice's image. It implements the symmetric dual network architecture along with two levels of restoration losses to achieve this proposed task. Three types of images  
Figure 3: Basic methodology of a demorphing morph attack detection model. are passed to the system for training, including the image of the criminal, the accomplice, and the morphed image. After training on these images, the FD-GAN can separate the accomplice's image from the morphed image by separating the identities of different participants in the morphed image. During the testing process, the FD-GAN takes the morphed image and the image of the criminal (live photo) to restore the image of the accomplice. Two types of losses are considered and monitored in this process. These losses are referred to as pixel-level restoration loss and feature-level restoration loss. The identity encoder is a convolution-based network that extracts the identity features from the received images. Extracted identity features are provided to the identity separation network, and the identity features of the accomplice are separated in this step. The identity features are passed through three cascaded residual facial restoration blocks to achieve complete facial restoration of the accomplice's image from the basic features. Restoration is done using up-sampling and convolution networks. The demorphing process has also been done using a convolutional neural network-based model to detect morphing attacks. The model uses two input images: potential morph and live photos [21]. The demorphing process revealed different facial features when applied to the morphed image and the live captured still. The technique mentioned in [2; 3] was used to create morphed images. Poisson image editing [29] was done to reduce the artifacts present in the morphed image and uplift the quality of the images. For verification that the traveler is the authentic owner of the passport, the image on the passport (potential morph) is compared with the live image of the traveler, and the model generates a demorphed output image. If the image on the passport is a morphed image, then the resulting demorphed output image is a new and different image from the live photo.