# Quadruplet Loss For Improving the Robustness to Face Morphing Attacks

arXiv: https://arxiv.org/abs/2402.14665 · HTML: https://mlsanity.com/paper/2402.14665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Deeply learned face representations are sparse, selective, and robust](https://mlsanity.com/paper/1412.1265/) (2014)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Face morphing detection in the presence of printing/scanning and heterogeneous image sources](https://mlsanity.com/paper/1901.08811/) (2019)
- [Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking](https://mlsanity.com/paper/1705.07222/) (2017)
- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN](https://mlsanity.com/paper/2009.01729/) (2020)
- [MorDeephy: Face Morphing Detection Via Fused Classification](https://mlsanity.com/paper/2208.03110/) (2022)
- [CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition](https://mlsanity.com/paper/2304.11668/) (2023)
- [DocFace: Matching ID Document Photos to Selfies](https://mlsanity.com/paper/1805.02283/) (2018)
- [Robustness of Facial Recognition to GAN-based Face-morphing Attacks](https://mlsanity.com/paper/2012.10548/) (2020)
- [TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks](https://mlsanity.com/paper/2401.11598/) (2024)
- [A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problems](https://mlsanity.com/paper/2002.11644/) (2020)

## Built on by (most cited first, 1 of 1)

- [MorphGuard: Morph Specific Margin Loss for Enhancing Robustness to Face Morphing Attacks](https://mlsanity.com/paper/2505.10497/) (2025)

## Abstract (arXiv)

Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalized characteristics, offer a promising solution. However, Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques, enabling the creation of fraudulent documents. In this study, we introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks. Our approach involves specific sampling of face image quadruplets, combined with face morphs, for network training. Experimental results demonstrate the efficiency of our strategy in improving the robustness of face recognition networks against morphing attacks.