The extensive utilization of biometric authentication systems have emanated
attackers / imposters to forge user identity based on morphed images. In this
attack, a synthetic image is produced and merged with genuine. Next, the
resultant image is user for authentication. Numerous deep neural convolutional
architectures have been proposed in literature for face Morphing Attack
Detection (MADs) to prevent such attacks and lessen the risks associated with
them. Although, deep learning models achieved optimal results in terms of
performance, it is difficult to understand and analyse these networks since
they are black box/opaque in nature. As a consequence, incorrect judgments may
be made. There is, however, a dearth of literature that explains
decision-making methods of black box deep learning models for biometric
Presentation Attack Detection (PADs) or MADs that can aid the biometric
community to have trust in deep learning-based biometric systems for
identification and authentication in various security applications such as
border control, criminal database establishment etc. In this work, we present a
novel visual explanation approach named Ensemble XAI integrating Saliency maps,
Class Activation Maps (CAM) and Gradient-CAM (Grad-CAM) to provide a more
comprehensive visual explanation for a deep learning prognostic model
(EfficientNet-B1) that we have employed to predict whether the input presented
to a biometric authentication system is morphed or genuine. The
experimentations have been performed on three publicly available datasets
namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack
(WMCA), and Makeup Induced Face Spoofing (MIFS). The experimental evaluations
affirms that the resultant visual explanations highlight more fine-grained
details of image features/areas focused by EfficientNet-B1 to reach decisions
along with appropriate reasoning.

本研究提出了一种名为 Ensemble XAI 的新型可视化解释方法，它整合了显著性地图、类激活映射和渐变 - 类激活映射，以提供更全面的可视化解释，用于预测输入是否为伪造的仿制品的深度学习预测模型。通过实验评估，该解释方法可以准确地说明深度学习模型的决策过程。

一种高效的集成可解释人工智能（XAI）方法用于人脸混合检测

An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face  Detection

Morphing attacks is a threat to biometric systems where the biometric
reference in an identity document can be altered. This form of attack presents
an important issue in applications relying on identity documents such as border
security or access control. Research in face morphing attack detection is
developing rapidly, however very few datasets with several forms of attacks are
publicly available. This paper bridges this gap by providing a new dataset with
four different types of morphing attacks, based on OpenCV, FaceMorpher,
WebMorph and a generative adversarial network (StyleGAN), generated with
original face images from three public face datasets. We also conduct extensive
experiments to assess the vulnerability of the state-of-the-art face
recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments
demonstrate that VGG-Face, while being less accurate face recognition system
compared to FaceNet, is also less vulnerable to morphing attacks. Also, we
observed that na\"ive morphs generated with a StyleGAN do not pose a
significant threat.

本研究提供了一个包含 4 种不同类型的变形攻击的新数据集，并通过基于 OpenCV、FaceMorpher、WebMorph 和生成对抗网络（StyleGAN）的变形攻击实验，评估了现有人脸识别系统的风险程度，其中发现 VGG-Face 虽然与 FaceNet 相比精度较低，但更加抗变形攻击。此外，本文指出了使用 StyleGAN 生成的低级变形攻击对人脸识别系统不构成重大威胁。