# Generalizability of CNN Architectures for Face Morph Presentation Attack

arXiv: https://arxiv.org/abs/2310.11105 · HTML: https://mlsanity.com/paper/2310.11105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Are GAN-based Morphs Threatening Face Recognition?](https://mlsanity.com/paper/2205.02496/) (2022)
- [Differential Morph Face Detection using Discriminative Wavelet Sub-bands](https://mlsanity.com/paper/2106.13178/) (2021)
- [Conditional Identity Disentanglement for Differential Face Morph Detection](https://mlsanity.com/paper/2107.02162/) (2021)

## Abstract (arXiv)

Automatic border control systems are wide spread in modern airports worldwide. Morphing attacks on face biometrics is a serious threat that undermines the security and reliability of face recognition systems deployed in airports and border controls. Therefore, developing a robust Machine Learning (ML) system is necessary to prevent criminals crossing borders with fake identifications especially since it has been shown that security officers cannot detect morphs better than machines. In this study, we investigate the generalization power of Convolutional Neural Network (CNN) architectures against morphing attacks. The investigation utilizes 5 distinct CNNs namely ShuffleNet, DenseNet201, VGG16, EffecientNet-B0 and InceptionResNet-v2. Each CNN architecture represents a well-known family of CNN models in terms of number of parameters, architectural design and performance across various computer vision applications. To ensure robust evaluation, we employ 4 different datasets (Utrecht, London, Defacto and KurdFace) that contain a diverse range of digital face images which cover variations in ethnicity, gender, age, lighting condition and camera setting. One of the fundamental concepts of ML system design is the ability to generalize effectively to previously unseen data, hence not only we evaluate the performance of CNN models within individual datasets but also explore their performance across combined datasets and investigating each dataset in testing phase only. Experimental results on more than 8 thousand images (genuine and morph) from the 4 datasets show that InceptionResNet-v2 generalizes better to unseen data and outperforms the other 4 CNN models.