Presentation attack is a challenging issue that persists in the security of
automatic fingerprint recognition systems. This paper proposes a novel
explainable residual slim network that detects the presentation attack by
representing the visual features in the input fingerprint sample. The
encoder-decoder of this network along with the channel attention block converts
the input sample into its heatmap representation while the modified residual
convolutional neural network classifier discriminates between live and spoof
fingerprints. The entire architecture of the heatmap generator block and
modified ResNet classifier works together in an end-to-end manner. The
performance of the proposed model is validated on benchmark liveness detection
competition databases i.e. Livdet 2011, 2013, 2015, 2017, and 2019 and the
classification accuracy of 96.86\%, 99.84\%, 96.45\%, 96.07\%, 96.27\% are
achieved on them, respectively. The performance of the proposed model is
compared with the state-of-the-art techniques, and the proposed method
outperforms state-of-the-art methods in benchmark protocols of presentation
attack detection in terms of classification accuracy.

本文提出了一种新颖的可解释的残差轻量级网络，通过表示输入指纹样本中的视觉特征来检测介绍攻击，并将其热度图表示，经过修改的残差卷积神经网络判别真假指纹，并在基准活体检测竞赛数据库 Livdet 2011，2013，2015，2017 和 2019 上分别实现了 96.86％，99.84％，96.45％，96.07％和 96.27％的分类准确度，相比于其他技术，该方法在介绍攻击检测基准协议方面的分类准确度表现更优秀。

EXPRESSNET: 可解释的残差轻量化网络用于指纹展示攻击检测

EXPRESSNET: An Explainable Residual Slim Network for Fingerprint  Presentation Attack Detection

Advances in image processing and analysis as well as machine learning
techniques have contributed to the use of biometric recognition systems in
daily people tasks. These tasks range from simple access to mobile devices to
tagging friends in photos shared on social networks and complex financial
operations on self-service devices for banking transactions. In China, the use
of these systems goes beyond personal use becoming a country's government
policy with the objective of monitoring the behavior of its population. On July
05th 2021, the Brazilian government announced acquisition of a biometric
recognition system to be used nationwide. In the opposite direction to China,
Europe and some American cities have already started the discussion about the
legality of using biometric systems in public places, even banning this
practice in their territory. In order to open a deeper discussion about the
risks and legality of using these systems, this work exposes the
vulnerabilities of biometric recognition systems, focusing its efforts on the
face modality. Furthermore, it shows how it is possible to fool a biometric
system through a well-known presentation attack approach in the literature
called morphing. Finally, a list of ten concerns was created to start the
discussion about the security of citizen data and data privacy law in the Age
of Artificial Intelligence (AI).

本文讨论了生物识别系统，特别是脸部模态的漏洞和如何通过伪造攻击的方式欺骗生物识别系统，并提出了十个问题，以开始在人工智能时代探讨市民数据安全和数据隐私问题。

人工智能时代巴西数据的风险？

The Brazilian Data at Risk in the Age of AI?

Face recognition has evolved as a prominent biometric authentication
modality. However, vulnerability to presentation attacks curtails its reliable
deployment. Automatic detection of presentation attacks is essential for secure
use of face recognition technology in unattended scenarios. In this work, we
introduce a Convolutional Neural Network (CNN) based framework for presentation
attack detection, with deep pixel-wise supervision. The framework uses only
frame level information making it suitable for deployment in smart devices with
minimal computational and time overhead. We demonstrate the effectiveness of
the proposed approach in public datasets for both intra as well as
cross-dataset experiments. The proposed approach achieves an HTER of 0% in
Replay Mobile dataset and an ACER of 0.42% in Protocol-1 of OULU dataset
outperforming state of the art methods.

本文提出了一种卷积神经网络的框架，用于检测面部识别中的展示攻击，该框架使用像素级监督的深度学习技术，可以使用帧级别信息进行部署，适用于在智能设备中的部署，该方法在公共数据集上验证有效性，并在多项实验中超过当前最先进的方法。