“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border crossing. Research has been focused on creating more accurate attack detection approaches by considering different image properties. However, all the attacks considered so far are based on manipulating facial landmarks localized in the morphed face images. In contrast, this work presents novel face morphing attacks based on image generated by generative adversarial networks. We present the MorGAN structure that considers the representation loss to successfully create realistic morphing attacks. Based on that, we present a novel face morphing attacks database (MorGAN database) that contains 1000 morph images for both, the proposed MorGAN and landmark-based attacks. We present vulnerability analysis of two face recognition approaches facing the proposed attacks. Moreover, the detectability of the proposed MorGAN attacks is studied, in the scenarios where this type of attacks is know and un- known. We concluded with pointing out the challenge of detecting such unknown novel attacks and an analysis of detection performances of different features in detecting such attacks.

MorGAN: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Generative Adversarial Network

The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of submitted MAD algorithms lacks robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different face morphing tools. Furthermore, multiple post-processings are applied on the reference images, e.g., print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential MAD algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3% D-EER) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.

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Deep Face Representations for Differential Morphing Attack Detection

Recent research found that attacks based on morphed face images, i.e., morphing attacks, pose a severe security risk to face recognition systems. A reliable morphing attack detection from a single face image remains a research challenge since cameras and morphing techniques used by an attacker are unknown at the time of classification. These issues are commonly overseen while many researchers report encouraging detection performance for training and testing morphing attack detection schemes on images obtained from a single face database employing a single morphing algorithm. In this work, a morphing attack detection system based on the analysis of Photo Response Non-Uniformity (PRNU) is presented. More specifically, spatial and spectral features extracted from PRNU patterns across image cells are analyzed. Differences of these features for bona fide and morphed images are estimated during a threshold-selection stage using the Dresden image database which is specifically built for PRNU analysis in digital image forensics. Cross-database evaluations are then conducted employing an ICAO compliant subset of the FRGCv2 database and a Print-Scan database which is a printed and scanned version of said FRGCv2 subset. Bona fide and morphed face images are automatically generated employing four different morphing algorithms. The proposed PRNU-based morphing attack detector is shown to robustly distinguish bona fide and morphed face images achieving an average D-EER of 11.2% in the best configuration. In scenarios where image sources and morphing techniques are unknown, it is shown to significantly outperform other previously established morphing attack detectors. Finally, the limitations and potential of the approach are demonstrated on a dataset of printed and scanned bona fide and morphed face images.

/pdf/detection-of-face-morphing-attacks-based-on-prnu-analysis-2xo724xxkj.pdf

Detection of Face Morphing Attacks Based on PRNU Analysis

Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features.

Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.

The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, Mix-FaceNets which are inspired by Mixed Depthwise Convolutional Kernels. Extensive experiment evaluations on Label Face in the Wild (LFW), Age-DB, MegaFace, and IARPA Janus Benchmarks IJB-B and IJB-C datasets have shown the effectiveness of our MixFaceNets for applications requiring extremely low computational complexity. Under the same level of computation complexity (≤ 500M FLOPs), our MixFaceNets outperform MobileFaceNets on all the evaluated datasets, achieving 99.60% accuracy on LFW, 97.05% accuracy on AgeDB-30, 93.60 TAR (at FAR1e-6) on MegaFace, 90.94 TAR (at FAR1e-4) on IJB-B and 93.08 TAR (at FAR1e-4) on IJB-C. With computational complexity between 500M and 1G FLOPs, our MixFaceNets achieved results comparable to the top-ranked models, while using significantly fewer FLOPs and less computation over-head, which proves the practical value of our proposed Mix-FaceNets. All training codes, pre-trained models, and training logs have been made available https://github.com/fdbtrs/mixfacenets.

MixFaceNets: Extremely Efficient Face Recognition Networks

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed margin constrain by proposing elastic margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the margin chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our elastic margin loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on six out of nine mainstream benchmarks.

Fadi Boutros

Papers

Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts

The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

MixFaceNets: Extremely Efficient Face Recognition Networks

ElasticFace: Elastic Margin Loss for Deep Face Recognition.

Self-restrained Triplet Loss for Accurate Masked Face Recognition