# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
## 5 Morphing Attack Detection
### MAD-Related Literature
There, a new, richer dataset is created and an assessment of two detection techniques based on support vector machines and local binary patterns, is provided. The authors in [67], followed the transfer-learning approach using the VGG19 and AlexNet DCNNs to detect both digital and print-scanned versions of morphed face images. The employed DCNNs were pre-trained (for the task of object classification on the ILSVRC dataset) and then further fine-tuned using morphed face images. At the inference phase, both AlexNet and VGG19 are fed with the same (test) image and the feature maps produced at their respective first fully connected layers are fused (i.e., concatenated) to form a single feature-vector which is provided to the classifier in order to decide whether the input was a genuine or a morphed face image. The goal was to utilize the full-scale feature-extraction potential of the convolutional layers of the DCNNs. The Probabilistic Collaborative Representation Classifier [68] was used for classification purposes. In [37], the authors achieve high detection ratios (surpassing the performance of rival deep-learning-based techniques) by pairing high-dimensional Local Binary Patterns (LBP) descriptors with a linear Support Vector Machine (SVM) for classification. LBPs are also used in [69]. The authors in [70] propose training SVMs using Weighted Local Magnitude Pattern, a descriptor similar to LPB, while a detector based on Scale-Invariant Feature Transform (SIFT) is proposed in [71]. Recently, a different approach, based on Photo Response Non-Uniformity (PRNU) analysis, is proposed in [9]. PRNU, which refers to the sensor's noise pattern, can be considered as a unique fingerprint for each camera sensor. As such, it presents itself with a number of advantages (large information content, present in every sensor, robust to lossy compression, filtering etc, but sensitive to non-linear warping) that make it suitable for morphing attack detection, with greater robustness and generalization potential than descriptor-based detectors. Treating the problem under the differential scenario, a generative adversarial network (GAN) for face de-morphing was proposed in [72] with the aim to restore the face of the accomplice that collaborated in the face morphing attack. To achieve this, the network analyses the live-capture and the morphed photo that was used in the utilized eMRTD. A similar approach for de-morphing has been proposed in [73], where the general idea is to \"subtract\" the information captured in-vivo (i.e., the live image) from the passport image and compare the residual of the de-morphing process, with the live capture. In case the passport image was the product of morphing between a \"criminal\" and an \"accomplice\", the residual image would resemble that of the accomplice (not present in the scene) and not live capture of the criminal. An autoencoder architecture based on VGG-face [74] was used for de-morphing. The authors in [50] propose a high-performance differential detector by using the feature vectors produced by ArcFace, a ResNet-based FRS. Detection is achieved by subtracting the feature vectors of the suspected image and the live capture. In [75] the authors use convolutional networks for image denoising and pyramid LPB for feature extraction while the same authors in [76], apart from using a DCNN-based denoising approach, utilize also AlexNet for feature extraction. Finally, Ref. [77] studied the problem of morphing attack detection when the morphing faces are produced using GANs instead of landmark-based approaches.