The accurate detection of ID card Presentation Attacks (PA) is becoming
increasingly important due to the rising number of online/remote services that
require the presentation of digital photographs of ID cards for digital
onboarding or authentication. Furthermore, cybercriminals are continuously
searching for innovative ways to fool authentication systems to gain
unauthorized access to these services. Although advances in neural network
design and training have pushed image classification to the state of the art,
one of the main challenges faced by the development of fraud detection systems
is the curation of representative datasets for training and evaluation. The
handcrafted creation of representative presentation attack samples often
requires expertise and is very time-consuming, thus an automatic process of
obtaining high-quality data is highly desirable. This work explores ID card
Presentation Attack Instruments (PAI) in order to improve the generation of
samples with four Generative Adversarial Networks (GANs) based image
translation models and analyses the effectiveness of the generated data for
training fraud detection systems. Using open-source data, we show that
synthetic attack presentations are an adequate complement for additional real
attack presentations, where we obtain an EER performance increase of 0.63%
points for print attacks and a loss of 0.29% for screen capture attacks.

通过使用基于生成对抗网络的四种图像翻译模型，本研究探讨了身份证展示攻击仪器在生成攻击样本方面的应用，并分析了生成数据在训练欺诈检测系统中的有效性。通过开源数据的实验结果，证明合成攻击展示样本可以作为真实攻击展示样本的补充，其中，印刷攻击的错误拒识率提高了 0.63%，屏幕截图攻击的拒识率下降了 0.29%。

基于神经迁移风格的公开集测试：身份证演示攻击检测

Open-Set: ID Card Presentation Attack Detection using Neural Transfer  Style

Fraudulent transactions and how to detect them remain a significant problem
for financial institutions around the world. The need for advanced fraud
detection systems to safeguard assets and maintain customer trust is paramount
for financial institutions, but some factors make the development of effective
and efficient fraud detection systems a challenge. One of such factors is the
fact that fraudulent transactions are rare and that many transaction datasets
are imbalanced; that is, there are fewer significant samples of fraudulent
transactions than legitimate ones. This data imbalance can affect the
performance or reliability of the fraud detection model. Moreover, due to the
data privacy laws that all financial institutions are subject to follow,
sharing customer data to facilitate a higher-performing centralized model is
impossible. Furthermore, the fraud detection technique should be transparent so
that it does not affect the user experience. Hence, this research introduces a
novel approach using Federated Learning (FL) and Explainable AI (XAI) to
address these challenges. FL enables financial institutions to collaboratively
train a model to detect fraudulent transactions without directly sharing
customer data, thereby preserving data privacy and confidentiality. Meanwhile,
the integration of XAI ensures that the predictions made by the model can be
understood and interpreted by human experts, adding a layer of transparency and
trust to the system. Experimental results, based on realistic transaction
datasets, reveal that the FL-based fraud detection system consistently
demonstrates high performance metrics. This study grounds FL's potential as an
effective and privacy-preserving tool in the fight against fraud.

研究介绍了使用联邦学习和可解释的人工智能的新方法来解决金融机构在欺诈检测方面面临的挑战，该方法能够在保护数据隐私的同时提高检测系统的性能，并具备透明性和可解释性。

透明和隐私：可解释人工智能和联邦学习在金融欺诈检测中的作用

Transparency and Privacy: The Role of Explainable AI and Federated  Learning in Financial Fraud Detection

Credit card fraud is a problem continuously faced by financial institutions
and their customers, which is mitigated by fraud detection systems. However,
these systems require the use of sensitive customer transaction data, which
introduces both a lack of privacy for the customer and a data breach
vulnerability to the card provider. This paper proposes a system for private
fraud detection on encrypted transactions using homomorphic encryption. Two
models, XGBoost and a feedforward classifier neural network, are trained as
fraud detectors on plaintext data. They are then converted to models which use
homomorphic encryption for private inference. Latency, storage, and detection
results are discussed, along with use cases and feasibility of deployment. The
XGBoost model has better performance, with an encrypted inference as low as
6ms, compared to 296ms for the neural network. However, the neural network
implementation may still be preferred, as it is simpler to deploy securely. A
codebase for the system is also provided, for simulation and further
development.

本文提出了一种使用同态加密进行私密欺诈检测的系统，在使用明文数据训练完 XGBoost 和神经网络后，将其转换为同态加密模型用于私有推断，比较两种模型的延迟、存储和检测结果，并探讨部署的可行性和应用案例。