11 research outputs found
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Face anti-spoofing has become an increasingly important and critical security
feature for authentication systems, due to rampant and easily launchable
presentation attacks. Addressing the shortage of multi-modal face dataset,
CASIA recently released the largest up-to-date CASIA-SURF Cross-ethnicity Face
Anti-spoofing(CeFA) dataset, covering 3 ethnicities, 3 modalities, 1607
subjects, and 2D plus 3D attack types in four protocols, and focusing on the
challenge of improving the generalization capability of face anti-spoofing in
cross-ethnicity and multi-modal continuous data. In this paper, we propose a
novel pipeline-based multi-stream CNN architecture called PipeNet for
multi-modal face anti-spoofing. Unlike previous works, Selective Modal Pipeline
(SMP) is designed to enable a customized pipeline for each data modality to
take full advantage of multi-modal data. Limited Frame Vote (LFV) is designed
to ensure stable and accurate prediction for video classification. The proposed
method wins the third place in the final ranking of Chalearn Multi-modal
Cross-ethnicity Face Anti-spoofing Recognition Challenge@CVPR2020. Our final
submission achieves the Average Classification Error Rate (ACER) of 2.21 with
Standard Deviation of 1.26 on the test set.Comment: Accepted to appear in CVPR2020 WM
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Face anti-spoofing is the key to preventing security breaches in biometric
recognition applications. Existing software-based and hardware-based face
liveness detection methods are effective in constrained environments or
designated datasets only. Deep learning method using RGB and infrared images
demands a large amount of training data for new attacks. In this paper, we
present a face anti-spoofing method in a real-world scenario by automatic
learning the physical characteristics in polarization images of a real face
compared to a deceptive attack. A computational framework is developed to
extract and classify the unique face features using convolutional neural
networks and SVM together. Our real-time polarized face anti-spoofing (PAAS)
detection method uses a on-chip integrated polarization imaging sensor with
optimized processing algorithms. Extensive experiments demonstrate the
advantages of the PAAS technique to counter diverse face spoofing attacks
(print, replay, mask) in uncontrolled indoor and outdoor conditions by learning
polarized face images of 33 people. A four-directional polarized face image
dataset is released to inspire future applications within biometric
anti-spoofing field.Comment: 14pages,8figure
Unified Physical-Digital Face Attack Detection
Face Recognition (FR) systems can suffer from physical (i.e., print photo)
and digital (i.e., DeepFake) attacks. However, previous related work rarely
considers both situations at the same time. This implies the deployment of
multiple models and thus more computational burden. The main reasons for this
lack of an integrated model are caused by two factors: (1) The lack of a
dataset including both physical and digital attacks with ID consistency which
means the same ID covers the real face and all attack types; (2) Given the
large intra-class variance between these two attacks, it is difficult to learn
a compact feature space to detect both attacks simultaneously. To address these
issues, we collect a Unified physical-digital Attack dataset, called
UniAttackData. The dataset consists of participations of 2 and 12
physical and digital attacks, respectively, resulting in a total of 29,706
videos. Then, we propose a Unified Attack Detection framework based on
Vision-Language Models (VLMs), namely UniAttackDetection, which includes three
main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring
unified and specific knowledge respectively; the Unified Knowledge Mining (UKM)
module, designed to capture a comprehensive feature space; and the Sample-Level
Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics.
These three modules seamlessly form a robust unified attack detection
framework. Extensive experiments on UniAttackData and three other datasets
demonstrate the superiority of our approach for unified face attack detection.Comment: 12 pages, 8 figure
Academic competitions
Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed
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