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

TL;DR

The MagFace model is a powerful alternative to the often-used ArcFace model in detecting attacks and can increase performance depending on the use case, and face embeddings stand out as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings.

Abstract

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.

Figures (24)

• Figure 1: Illustration of morphed face images created using different morphing approaches. The images on the left and on the right represent the corresponding two bona fide face images. Face images are republished from uncw_morph_nodate under a CC BY license, with permission from Prof. Karl Ricanek Jr, University of North Carolina at Wilmington, original copyright 2006.
• Figure 2: General workflow of our proposed pipeline for image pre-selection. Embeddings were extracted from one sample of each subject. Distances between embeddings were calculated. Faces were paired based on a low distance between embeddings. Pairs were then morphed, and morphed images were verified against bona fide probe images. Furthermore, Morphing Attack Detection has been conducted. The image pre-selection steps are further illustrated in Algorithm \ref{['algo1']}. The processing steps were performed using different FRSs and different morphing algorithms. Face images are republished from uncw_morph_nodate under a CC BY license, with permission from Prof. Karl Ricanek Jr, University of North Carolina at Wilmington, original copyright 2006.
• Figure 3: Morphing Attack Potential (MAP). The MAP is a matrix describing the success of a data set of morphed images to fool a set of FRSs using multiple attack attempts. Several FRSs (x-axis) are attacked with several mated Morphing Attack attempts (y-axis). The element of a MAP matrix describes the proportion of successful verifications of both attackers (i.e., both contributing subjects of each morph) at a given number of attempts (i.e., number of different bona fide images for both subjects) and with a particular number of fooled FRSs. Note that MAP was calculated as a decimal fraction within the range $[0;1]$.
• Figure 4: D-MAD pipeline. ArcFace or MagFace embeddings were extracted from bona fide images and morphed images. Differential embeddings have been created by subtraction of either the embeddings of a bona fide image from a morphed image or by the subtraction of a bona fide image from a different bona fide image of the same data subject. The differential vectors have been re-scaled to $N(0,1)$. A classifier was trained (on ArcFace and MagFace differential embeddings, separately) to differentiate between bona fide images and morphed images. Face images are republished from uncw_morph_nodate under a CC BY license, with permission from Prof. Karl Ricanek Jr, University of North Carolina at Wilmington, original copyright 2006.
• Figure 5: Mated morphs comparison success rates for different image pre-selection embeddings. prodAvgMMPMRs (y-axes) are plotted for different pre-selection methods (x-axis & color-coded). Density is plotted in horizontal direction. Median values are illustrated by horizontal black bars. The same pairs were morphed by different morphing methods (rows). Random assignments of the morphing pairs are displayed in the left-most column. All morphs were verified using ArcFace and MagFace (columns). See \ref{['fig:res:MMPMR_MORPH_DF_VF']} for verifications using DeepFace and VGG-Face. Note that prodAvgMMPMR was calculated as a decimal fraction within the range $[0;1]$.