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

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

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure

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

Deep Spiking Neural Network for Video-based Disguise Face Recognition Based on Dynamic Facial Movements

With the increasing popularity of social media andsmart devices, the face as one of the key biometrics becomesvital for person identification. Amongst those face recognitionalgorithms, video-based face recognition methods could make useof both temporal and spatial information just as humans do toachieve better classification performance. However, they cannotidentify individuals when certain key facial areas like eyes or noseare disguised by heavy makeup or rubber/digital masks. To thisend, we propose a novel deep spiking neural network architecturein this study. It takes dynamic facial movements, the facial musclechanges induced by speaking or other activities, as the sole input.An event-driven continuous spike-timing dependent plasticitylearning rule with adaptive thresholding is applied to train thesynaptic weights. The experiments on our proposed video-baseddisguise face database (MakeFace DB) demonstrate that theproposed learning method performs very well - it achieves from95% to 100% correct classification rates under various realisticexperimental scenario

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-