# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Methods of Morph Attack Detection
#### 2.4.2 Feature Extraction and Comparison  
In feature extraction methodology, a deep learning-based model known as the deep face model is used to extract features from the live image and potentially morphed images. A machine learning-based classifier is employed for categorizing the query images built on the extracted features. The differential morph detection method has been used extensively in the literature by utilizing different techniques of feature extraction and comparison [1; 20; 21; 22; 23; 24; 25]. In [5], a robust differential morphing attack detection model was presented that efficiently classified morphed images generated from four different morphing tools. The basic methodology of a differential morph attack detection model is presented in Figure 4. During testing, the analyzed images contained unprocessed, resized, JPG2000 compressed, and print-scanned images. A pre-trained deep face recognition network was used as a feature extractor, and the proposed morph attack detection classifier was trained on the lowest layer. The deep face representation of the morphed image was expected to be different than the original image because it contained features of two images. The input images of potential morph and live pictures were pre-processed. The pre-processed images were provided to the deep-face neural networks for feature extraction. ArcFace and Facenet neural networks were used for feature extraction. Multidimensional scaling of the difference of feature vectors was also done. The deep face representations were combined and processed by a machine learning-based classifier for training to differentiate between morphed and original images. A potential morph and a test image were provided to the model in pairs during the testing process. The neural network extracted the features from the images, and the features were combined and forwarded to the classifier for classifying the image as original or morph. Results indicated a higher level of accuracy for MAD in comparison with the aforementioned works. In the study [20], an inherent vulnerability of the neural network in its learning process was highlighted, and it was proposed that this vulnerability was the root cause of their weakness against morphing attacks. Therefore, training data was introduced with different alterations to increase the robustness of the neural networks against such attacks. Layer-wise relevance propagation (LRP) of the neural networks was also analyzed to observe the variations in the decision-making process of the networks [30]. In this work, the neural network was guided to take into account the whole information of the image and to focus on specific areas for observing traces of image morphing. Two important attack scenarios were included in the study: semantic attack (normal morph attack) and adversarial attack (in which the morph creator has access to the neural network and the creator can test the system's response and manipulate its weights). A VGG19 neural network was used that contained two output neurons for classification of the original image or the morphed image. Gaussian and motion blur were used to reduce the artifacts in the morphed image to prevent the classification of these images based on these factors. Using layer-wise level propagation (LRP) [31], it was found that the neural network focused on the eye portion of images for morph detection and did not pay attention to the rest of the image. The type of information, location of information, and amount of information from images available to the neural network were controlled to force the network to consider all the information from the image to make decisions. For this purpose, partially morphed images were used in which only some portion was taken from the morphed image, and the rest of the portion of the image was from an input aligned image. Partial morphs were blended in using Poisson image editing around the eyes, nose, and mouth. The importance of the initial convolution layer was transmitted using flat-weight decomposition [32].