Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD, FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pre-training. In this work, we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end, we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework, MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD, we publicly release the implementation of MADation at https://github.com/gurayozgur/MADation
Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection algorithms. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.
Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, none of the existing research on DiMs have leveraged the iterative nature of DiMs and left the DiM model as a black box, treating it no differently than one would a Generative Adversarial Network (GAN) or Varational AutoEncoder (VAE). We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based heuristic function. We compare our proposed algorithm against ten other state-of-the-art morphing algorithms using the open-source SYN-MAD 2022 competition dataset. We find that our proposed algorithm is unreasonably effective, fooling all of the tested FR systems with an MMPMR of 100%, outperforming all other morphing algorithms compared.
Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high quality face morphs; however, they require a high number of network function evaluations (NFE) to create the morphs. We propose a new DiM pipeline, Fast-DiM, which can create morphs of a similar quality but with fewer NFE. We investigate the ODE solvers used to solve the Probability Flow ODE and the impact they have on the the creation of face morphs. Additionally, we employ an alternative method for encoding images into the latent space of the Diffusion model by solving the Probability Flow ODE as time runs forwards. Our experiments show that we can reduce the NFE by upwards of 85% in the encoding process while experiencing only 1.6\% reduction in Mated Morph Presentation Match Rate (MMPMR). Likewise, we showed we could cut NFE, in the sampling process, in half with only a maximal reduction of 0.23% in MMPMR.
The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion-based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets.
Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining.
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.
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.
Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multispectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.
Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate $526$ unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. {{We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS}}. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: \url{https://github.com/jagmohaniiit/LatentCompositionCode}
Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods.
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people's privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.
The supervised-learning-based morphing attack detection (MAD) solutions achieve outstanding success in dealing with attacks from known morphing techniques and known data sources. However, given variations in the morphing attacks, the performance of supervised MAD solutions drops significantly due to the insufficient diversity and quantity of the existing MAD datasets. To address this concern, we propose a completely unsupervised MAD solution via self-paced anomaly detection (SPL-MAD) by leveraging the existing large-scale face recognition (FR) datasets and the unsupervised nature of convolutional autoencoders. Using general FR datasets that might contain unintentionally and unlabeled manipulated samples to train an autoencoder can lead to a diverse reconstruction behavior of attack and bona fide samples. We analyze this behavior empirically to provide a solid theoretical ground for designing our unsupervised MAD solution. This also results in proposing to integrate our adapted modified self-paced learning paradigm to enhance the reconstruction error separability between the bona fide and attack samples in a completely unsupervised manner. Our experimental results on a diverse set of MAD evaluation datasets show that the proposed unsupervised SPL-MAD solution outperforms the overall performance of a wide range of supervised MAD solutions and provides higher generalizability on unknown attacks.
Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a sophisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalising the task of morphing detection to unseen scenarios.
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.
Face morphing attack detection is challenging and presents a concrete and severe threat for face verification systems. Reliable detection mechanisms for such attacks, which have been tested with a robust cross-database protocol and unknown morphing tools still is a research challenge. This paper proposes a framework following the Few-Shot-Learning approach that shares image information based on the siamese network using triplet-semi-hard-loss to tackle the morphing attack detection and boost the clustering classification process. This network compares a bona fide or potentially morphed image with triplets of morphing and bona fide face images. Our results show that this new network cluster the data points, and assigns them to classes in order to obtain a lower equal error rate in a cross-database scenario sharing only small image numbers from an unknown database. Few-shot learning helps to boost the learning process. Experimental results using a cross-datasets trained with FRGCv2 and tested with FERET and the AMSL open-access databases reduced the BPCER10 from 43% to 4.91% using ResNet50 and 5.50% for MobileNetV2.
Face Recognition systems (FRS) have been found to be vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction for generating face-morphing attacks in 3D. To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects. The proposed method generates 3D face morphing by projecting the input 3D face point clouds onto depth maps and 2D color images, followed by image blending and wrapping operations performed independently on the color images and depth maps. We then back-projected the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes owing to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments were conducted on the newly generated 3D face dataset comprising 675 3D scans corresponding to 41 unique data subjects and a publicly available database (Facescape) with 100 data subjects. Experiments were performed to benchmark the vulnerability of the {proposed 3D morph-generation scheme against} automatic 2D, 3D FRS, and human observer analysis. We also presented a quantitative assessment of the quality of the generated 3D face-morphing models using eight different quality metrics. Finally, we propose three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face morphing attack detection techniques.
Face morphing attacks can compromise Face Recognition System (FRS) by exploiting their vulnerability. Face Morphing Attack Detection (MAD) techniques have been developed in recent past to deter such attacks and mitigate risks from morphing attacks. MAD algorithms, as any other algorithms should treat the images of subjects from different ethnic origins in an equal manner and provide non-discriminatory results. While the promising MAD algorithms are tested for robustness, there is no study comprehensively bench-marking their behaviour against various ethnicities. In this paper, we study and present a comprehensive analysis of algorithmic fairness of the existing Single image-based Morph Attack Detection (S-MAD) algorithms. We attempt to better understand the influence of ethnic bias on MAD algorithms and to this extent, we study the performance of MAD algorithms on a newly created dataset consisting of four different ethnic groups. With Extensive experiments using six different S-MAD techniques, we first present benchmark of detection performance and then measure the quantitative value of the algorithmic fairness for each of them using Fairness Discrepancy Rate (FDR). The results indicate the lack of fairness on all six different S-MAD methods when trained and tested on different ethnic groups suggesting the need for reliable MAD approaches to mitigate the algorithmic bias.
Face morphing attack detection is a challenging task. Automatic classification methods and manual inspection are realised in automatic border control gates to detect morphing attacks. Understanding how a machine learning system can detect morphed faces and the most relevant facial areas is crucial. Those relevant areas contain texture signals that allow us to separate the bona fide and the morph images. Also, it helps in the manual examination to detect a passport generated with morphed images. This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter to select the most relevant and less redundant features. This selection allows us to reduce the workload and know the exact localisation of such areas to understand the morphing impact and create a robust classifier. The best results were obtained for the method based on Conditional Mutual Information and Shape features using only 500 features for FERET images and 800 features for FRGCv2 images from 1,048 features available. The eyes and nose are identified as the most critical areas to be analysed.