Explainable Visualization for Morphing Attack Detection

Detecting morphed face images has become critical for maintaining trust in automated facial biometric verification systems. It is well demonstrated that better biometric performance of the Face Recognition System (FRS) results in higher vulnerability to face morphing attacks. Morphing can be understood as a technique to combine two or more look-alike facial images corresponding to the attacker and an accomplice, who could apply for a valid passport by exploiting the accomplice’s identity. Morphing Attack Detection (MAD), with the help of Convolutional Neural Networks (CNN), has demonstrated good performance in detecting morphed images. However, they lack transparency, and it is unclear how they differentiate between bona fide and morphed facial images. As a result, this phenomenon needs careful consideration for safety and security-related applications. This paper will explore Layer-wise Relevance Propagation (LRP) to determine the most relevant features. We fine-tune a VGG pre-trained network for face morphing attack detection and LRP is then used to investigate the decision-making processes to understand what input pixels take part in the attack detection. This paper shows that CNN considers only a small part of the image, usually around the eyes, nose, and mouth

Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection

Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphing Attacks. We build upon previous embedding studies in both use cases using the MagFace model. For the first objective, we employ an pre-selection algorithm that pairs individuals based on face embedding similarity. We quantify the attack potential of differently morphed face images to compare the usability of pre-selection in automatically generating numerous successful Morphing Attacks. Regarding the second objective, we compare embeddings from two state-of-the-art face recognition systems in terms of their ability to detect Morphing Attacks. Our findings demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Both open-source and COTS face recognition systems are susceptible to generated attacks, particularly when pre-selection is based on embeddings rather than random pairing which was only constrained by soft biometrics. More accurate face recognition systems exhibit greater vulnerability to attacks, with COTS systems being the most susceptible. Additionally, MagFace embeddings serve as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the advantages of face embeddings in more effective image pre-selection for face morphing and accurate detection of morphed face images. This is supported by extensive analysis of various designed attacks. The MagFace model proves to be a powerful alternative to the commonly used ArcFace model for both objectives, pre-selection and attack detection

Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network

Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwanted consequences, both for security and society. In this paper, we present a new reference-based/Differential Morphing Attack Detection (MAD) method to detect newborn morphing images using Wavelet Scattering Network (WSN). We propose a two-layer WSN with 250 $\times$ 250 pixels and six rotations of wavelets per layer, resulting in 577 paths. The proposed approach is validated on a dataset of 852 bona fide images and 2460 morphing images constructed using face images of 42 unique newborns. The obtained results indicate a gain of over 10\% in detection accuracy over other existing D-MAD techniques.Comment: accepted in 5th International Conference on Bio-engineering for Smart Technologies (BIO-SMART 2023

Deep Face Morph Detection Based on Wavelet Decomposition

Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this dissertation, we delve into data-driven morph detection which is of great significance in terms of national security. We propose several wavelet-based face morph detection schemes which employ some of the computer vision algorithms such as image wavelet analysis, group sparsity, feature selection, and the visual attention mechanisms. Wavelet decomposition enables us to leverage the fine-grained frequency content of an image to boost localizing manipulated areas in an image. Our methodologies are as follows: (1) entropy-based single morph detection, (2) entropy-based differential morph detection, (3) morph detection using group sparsity, and (4) Attention aware morph detection. In the first methodology, we harness mismatches between the entropy distribution of wavelet subbands corresponding to a pair of real and morph images to find a subset of most discriminative wavelet subbands which leads to an increase of morph detection accuracy. As the second methodology, we adopt entropy-based subband selection to tackle differential morph detection. In the third methodology, group sparsity is leveraged for subband selection. In other words, adding a group sparsity constraint to the loss function of our DNN leads to an implicit subband selection. Our fourth methodology consists of different types of visual attention mechanisms such as convolutional block attention modules and self-attention resulting in boosting morph detection accuracy. We demonstrate efficiency of our proposed algorithms through several morph datasets via extensive evaluations as well as visualization methodologies