SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available.Comment: Accepted at CVPR workshop 202

Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD-2013 database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.Comment: accepted for publication at EUSIPCO202

Fairness in Face Presentation Attack Detection

Face presentation attack detection (PAD) is critical to secure face recognition (FR) applications from presentation attacks. FR performance has been shown to be unfair to certain demographic and non-demographic groups. However, the fairness of face PAD is an understudied issue, mainly due to the lack of appropriately annotated data. To address this issue, this work first presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining several well-known PAD datasets where we provide seven human-annotated attribute labels. This work then comprehensively analyses the fairness of a set of face PADs and its relation to the nature of training data and the Operational Decision Threshold Assignment (ODTA) on different data groups by studying four face PAD approaches on our CAAD-PAD. To simultaneously represent both the PAD fairness and the absolute PAD performance, we introduce a novel metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on CAAD-PAD show that the training data and ODTA induce unfairness on gender, occlusion, and other attribute groups. Based on these analyses, we propose a data augmentation method, FairSWAP, which aims to disrupt the identity/semantic information and guide models to mine attack cues rather than attribute-related information. Detailed experimental results demonstrate that FairSWAP generally enhances both the PAD performance and the fairness of face PAD

Response of Three Kinds of Detoxifying Enzymes from Odontotermes formosanus (Shiraki) to the Stress Caused by Serratia marcescens Bizio (SM1)

Subterranean termite Odontotermes formosanus (Shiraki) (Blattodea: Isoptera: Termitidae), is a pest species found in forests and dams. Serratia marcescens Bizio (SM1) has a potential pathogenic effect on O. formosanus. However, the response of detoxifying enzymes to exposure by S. marcescens in O. formosanus has not been studied. In the present work, 20 detoxifying enzyme genes, including 6 glutathione S-transferases (GSTs), 5 UDP glycosyltransferases (UGTs) and 9 Cytochrome P450s (CYPs), were identified from the O. formosanus transcriptome dataset by bioinformatics analysis. Furthermore, the effects of SM1 infection on the transcription levels of detoxifying enzyme genes (GSTs, UGTs and CYPs) in O. formosanus were determined. The results showed that the expression of all detoxifying enzyme gene, except one GST, in O. formosanus were altered in response to the infection by SM1. The response of GSTs, UGTs and CYPs to SM1 in O. formosanus suggested that they may play an important role in the defense against bacterial infection such as SM1, and implies that termites have evolved a complex immune response to potential pathogens

ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation

Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, several previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes, e.g. identity, hairstyle, pose, or expression. Most of these works require designing special loss functions and training dedicated network architectures. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN's latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in state-of-the-art pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. The variations in our generated images are not limited to specific attributes as ExFaceGAN explicitly aims at disentangling identity information, while other visual attributes are randomly drawn from a learned GAN latent space. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models.Comment: Accepted at IJCB 202

MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders

Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder models have overcome the GAN limitations, leading to high reconstruction fidelity. This theoretically makes them a perfect candidate to perform representation-level face morphing. This work investigates using diffusion autoencoders to create face morphing attacks by comparing them to a wide range of image-level and representation-level morphs. Our vulnerability analyses on four state-of-the-art face recognition models have shown that such models are highly vulnerable to the created attacks, the MorDIFF, especially when compared to existing representation-level morphs. Detailed detectability analyses are also performed on the MorDIFF, showing that they are as challenging to detect as other morphing attacks created on the image- or representation-level. Data and morphing script are made public: https://github.com/naserdamer/MorDIFF.Comment: Accepted at the 11th International Workshop on Biometrics and Forensics 2023 (IWBF 2023

The Immediate Economic Impact of Maternal Deaths on Rural Chinese Households

OBJECTIVE: To identify the immediate economic impact of maternal death on rural Chinese households. METHODS: Results are reported from a study that matched 195 households who had suffered a maternal death to 384 households that experienced a childbirth without maternal death in rural areas of three provinces in China, using quantitative questionnaire to compare differences of direct and indirect costs between two groups. FINDINGS: The direct costs of a maternal death were significantly higher than the costs of a childbirth without a maternal death (US$4,119 vs.$370, p<0.001). More than 40% of the direct costs were attributed to funeral expenses. Hospitalization and emergency care expenses were the largest proportion of non-funeral direct costs and were higher in households with maternal death than the comparison group (US$2,248 vs.$305, p<0.001). To cover most of the high direct costs, 44.1% of affected households utilized compensation from hospitals, and the rest affected households (55.9%) utilized borrowing money or taking loans as major source of money to offset direct costs. The median economic burden of the direct (and non-reimbursed) costs of a maternal death was quite high--37.0% of the household's annual income, which was approximately 4 times as high as the threshold for an expense being considered catastrophic. CONCLUSION: The immediate direct costs of maternal deaths are extremely catastrophic for the rural Chinese households in three provinces studied