mlsanityData to 7 Oct 2026

Find the Differences: Differential Morphing Attack Detection vs Face Recognition

arXiv 2604.14734 ↗First posted 16 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

Morphing is a challenge to face recognition (FR) for which several morphing attack detection solutions have been proposed. We argue that face recognition and differential morphing attack detection (D-MAD) in principle perform very similar tasks, which we support by comparing an FR system with two existing D-MAD approaches. We also show that currently used decision thresholds inherently lead to FR systems being vulnerable to morphing attacks and that this explains the tradeoff between performance on normal images and vulnerability to morphing attacks. We propose using FR systems that are already in place for morphing detection and introduce a new evaluation threshold that guarantees an upper limit to the vulnerability to morphing attacks - even of unknown types.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 16 Apr 2026.

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14 of 33 references.

References · 14

  1. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
  2. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 733
  3. 2021SynFace: Face Recognition with Synthetic Datacited by 106
  4. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 63
  5. 2020Deep Face Representations for Differential Morphing Attack Detectioncited by 46
  6. 2017von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verificationcited by 45
  7. 2023MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoderscited by 44
  8. 2020Morphing Attack Detection -- Database, Evaluation Platform and Benchmarkingcited by 38
  9. 2019Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectancecited by 15
  10. 2021Conditional Identity Disentanglement for Differential Face Morph Detectioncited by 12
  11. 2022KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognitioncited by 11
  12. 2022On the detection of morphing attacks generated by GANscited by 9
2 more
  1. 2021Worst-Case Morphs: a Theoretical and a Practical Approachcited by 3
  2. 2023Worst-Case Morphs using Wasserstein ALI and Improved MIPGANcited by 2

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