Revelio: A Modular and Effective Framework for Reproducible Training and Evaluation of Morphing Attack Detectors

Morphing Attack, i.e. the elusion of face verification systems through a facial morphing operation between a criminal and an accomplice, has recently emerged as a serious security threat. Despite the importance of this kind of attack, the development and comparison of Morphing Attack Detection (MAD) methods is still a challenging task, especially with deep learning approaches. Specifically, the lack of public datasets, the absence of common training and validation protocols, and the limited release of public source code hamper the reproducibility and objective comparison of new MAD systems. Usually, these aspects are mainly due to privacy concerns, that limit data transfers and storage, and to the recent introduction of the MAD task. Therefore, in this paper, we propose and publicly release Revelio, a modular framework for the reproducible development and evaluation of MAD systems. We include an overview of the modules, and describe the plugin system providing the possibility of extending native components with new functionalities. An extensive cross-datasets experimental evaluation is conducted to validate the framework and the performance of trained models on several publicly-released datasets, and to deeply analyze the main challenges in the MAD task based on single input images. We also propose a new metric, namely WAED, to summarize in a single value the error-based metrics commonly used in the MAD task, computed over different datasets, thus facilitating the comparative evaluation of different approaches. Finally, by exploiting Revelio, a new state-of-the-art MAD model (on SOTAMD single-image benchmark) is proposed and released

Analyzing eyebrow region for morphed image detection

Facial images in passports are designated as primary identifiers for the verification of travelers according to the International Civil Aviation Organization (ICAO). Hence, it is important to ascertain the sanctity of the facial images stored in the electronic Machine-Readable Travel Document (eMRTD). With the introduction of automated border control (ABC) systems that rely on face recognition for the verification of travelers, it is even more crucial to have a system to ensure that the image stored in the eMRTD is free from any alteration that can hinder or abuse the normal working of a facial recognition system. One such attack against these systems is the face-morphing attack. Even though many techniques exist to detect morphed images, morphing algorithms are also improving to evade these detections. In this work, we analyze the eyebrow region for morphed image detection. The proposed method is based on analyzing the frequency content of the eyebrow region. The method was evaluated on two datasets that each consisted of morphed images created using two algorithms. The findings suggest that the proposed method can serve as a valuable tool in morphed image detection, and can be used in various applications where image authenticity is critical

Face morphing detection in the presence of printing/scanning and heterogeneous image sources

Face morphing represents nowadays a big security threat in the context of electronic identity documents as well as an interesting challenge for researchers in the field of face recognition. Despite of the good performance obtained by state-of-the-art approaches on digital images, no satisfactory solutions have been identified so far to deal with cross-database testing and printed-scanned images (typically used in many countries for document issuing). In this work, novel approaches are proposed to train Deep Neural Networks for morphing detection: in particular generation of simulated printed-scanned images together with other data augmentation strategies and pre-training on large face recognition datasets, allowed to reach state-of-the-art accuracy on challenging datasets from heterogeneous image sources

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

A Double Siamese Framework for Differential Morphing Attack Detection

Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Development of a Group Dynamic Functional Connectivity Pipeline for Magnetoencephalography Data and its Application to the Human Face Processing Network

Since its inception, functional neuroimaging has focused on identifying sources of neural activity. Recently, interest has turned to the analysis of connectivity between neural sources in dynamic brain networks. This new interest calls for the development of appropriate investigative techniques. A problem occurs in connectivity studies when the differing networks of individually analyzed subjects must be reconciled. One solution, the estimation of group models, has become common in fMRI, but is largely untried with electromagnetic data. Additionally, the assumption of stationarity has crept into the field, precluding the analysis of dynamic systems. Group extensions are applied to the sparse irMxNE localizer of MNE-Python. Spectral estimation requires individual source trials, and a multivariate multiple regression procedure is established to accomplish this based on the irMxNE output. A program based on the Fieldtrip software is created to estimate conditional Granger causality spectra in the time-frequency domain based on these trials. End-to-end simulations support the correctness of the pipeline with single and multiple subjects. Group-irMxNE makes no attempt to generalize a solution between subjects with clearly distinct patterns of source connectivity, but shows signs of doing so when subjects patterns of activity are similar. The pipeline is applied to MEG data from the facial emotion protocol in an attempt to validate the Adolphs model. Both irMxNE and Group-irMxNE place numerous sources during post-stimulus periods of high evoked power but neglect those of low power. This identifies a conflict between power-based localizations and information-centric processing models. It is also noted that neural processing is more diffuse than the neatly specified Adolphs model indicates. Individual and group results generally support early processing in the occipital, parietal, and temporal regions, but later stage frontal localizations are missing. The morphing of individual subjects\u27 brain topology to a common source-space is currently inoperable in MNE. MEG data is therefore co-registered directly onto an average brain, resulting in loss of accuracy. For this as well as reasons related to uneven power and computational limitations, the early stages of the Adolphs model are only generally validated. Encouraging results indicate that actual non-stationary group connectivity estimates are produced however