A secure fingerprint recognition system must contain both a presentation
attack (i.e., spoof) detection and recognition module in order to protect users
against unwanted access by malicious users. Traditionally, these tasks would be
carried out by two independent systems; however, recent studies have
demonstrated the potential to have one unified system architecture in order to
reduce the computational burdens on the system, while maintaining high
accuracy. In this work, we leverage a vision transformer architecture for joint
spoof detection and matching and report competitive results with
state-of-the-art (SOTA) models for both a sequential system (two ViT models
operating independently) and a unified architecture (a single ViT model for
both tasks). ViT models are particularly well suited for this task as the ViT's
global embedding encodes features useful for recognition, whereas the
individual, local embeddings are useful for spoof detection. We demonstrate the
capability of our unified model to achieve an average integrated matching (IM)
accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors. This is
comparable to IM accuracy of 98.95% of our sequential dual-ViT system, but with
~50% of the parameters and ~58% of the latency.

本研究提出使用 Vision Transformer（ViT）模型的统一系统架构，实现虚假检测和匹配，并取得了与最先进（SOTA）的顺序系统相当的 98.87% 的平均集成匹配（IM）准确率，但参数只有后者的约 50％，延迟只有后者的约 58％。该方法适用于安全指纹识别等场景。

ViT 统一：联合指纹识别和呈现攻击检测

ViT Unified: Joint Fingerprint Recognition and Presentation Attack  Detection

Prior studies show that the key to face anti-spoofing lies in the subtle
image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge,
Moire pattern, and many others. Designing a generic anti-spoofing model to
estimate those spoof traces can improve not only the generalization of the
spoof detection, but also the interpretability of the model's decision. Yet,
this is a challenging task due to the diversity of spoof types and the lack of
ground truth in spoof traces. This work designs a novel adversarial learning
framework to disentangle the spoof traces from input faces as a hierarchical
combination of patterns at multiple scales. With the disentangled spoof traces,
we unveil the live counterpart of the original spoof face, and further
synthesize realistic new spoof faces after a proper geometric correction. Our
method demonstrates superior spoof detection performance on both seen and
unseen spoof scenarios while providing visually convincing estimation of spoof
traces. Code is available at this https URL

通过对抗性学习框架，设计出一种方法以多尺度模式的等级组合方式将伪造痕迹从输入面部中分离出来，并进一步经过适当的几何修正合成出另一张逼真伪造面孔图像，从而具有卓越的伪造检测性能。

关于解离面部欺骗的伪迹的通用面部反欺骗技术

On Disentangling Spoof Trace for Generic Face Anti-Spoofing

State-of-the-art spoof detection methods tend to overfit to the spoof types
seen during training and fail to generalize to unknown spoof types. Given that
face anti-spoofing is inherently a local task, we propose a face anti-spoofing
framework, namely Self-Supervised Regional Fully Convolutional Network
(SSR-FCN), that is trained to learn local discriminative cues from a face image
in a self-supervised manner. The proposed framework improves generalizability
while maintaining the computational efficiency of holistic face anti-spoofing
approaches (< 4 ms on a Nvidia GTX 1080Ti GPU). The proposed method is
interpretable since it localizes which parts of the face are labeled as spoofs.
Experimental results show that SSR-FCN can achieve TDR = 65% @ 2.0% FDR when
evaluated on a dataset comprising of 13 different spoof types under unknown
attacks while achieving competitive performances under standard benchmark
datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).

该研究提出了一种基于自监督区域全卷积网络（SSR-FCN）的人脸反欺诈框架，通过自监督方式学习面部图像的局部判别性线索，从而提高了泛化性能，同时保持了整体人脸反欺诈方法的计算效率，具有可解释性，本方法在未知攻击下所达到的 TDR=65%@2.0% FDR，表现出色。