Quadruplet Loss For Improving the Robustness to Face Morphing Attacks
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 22 Feb 2024.
Builds on
24 of 47 references.
References · 24
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
- 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
- 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 733
- 2021MagFace: A Universal Representation for Face Recognition and Quality Assessmentcited by 332
- 2018Additive Margin Softmax for Face Verificationcited by 275
- 2017Working hard to know your neighbor's margins: Local descriptor learning losscited by 212
- 2017Beyond triplet loss: a deep quadruplet network for person re-identificationcited by 172
- 2019Probabilistic Face Embeddingscited by 171
- 2014Deeply learned face representations are sparse, selective, and robustcited by 56
12 more
- 2020Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detectioncited by 46
- 2023MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoderscited by 44
- 2019Face morphing detection in the presence of printing/scanning and heterogeneous image sourcescited by 22
- 2017Quadruplet Network with One-Shot Learning for Fast Visual Object Trackingcited by 12
- 2022OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglementcited by 10
- 2020MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GANcited by 9
- 2022MorDeephy: Face Morphing Detection Via Fused Classificationcited by 7
- 2023CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognitioncited by 7
- 2018DocFace: Matching ID Document Photos to Selfiescited by 6
- 2020Robustness of Facial Recognition to GAN-based Face-morphing Attackscited by 2
- 2024TetraLoss: Improving the Robustness of Face Recognition against Morphing Attackscited by 2
- 2020A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problemscited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.