mlsanityData to 14 Oct 2026

WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation

arXiv 2507.12493 ↗First posted 15 Jul 2025cs.GRcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Biometric face morphing poses a critical challenge to identity verification systems, undermining their security and robustness. To address this issue, we propose WaFusion, a novel framework combining wavelet decomposition and diffusion models to generate high-quality, realistic morphed face images efficiently. WaFusion leverages the structural details captured by wavelet transforms and the generative capabilities of diffusion models, producing face morphs with minimal artifacts. Experiments conducted on FERET, FRGC, FRLL, and WVU Twin datasets demonstrate WaFusion's superiority over state-of-the-art methods, producing high-resolution morphs with fewer artifacts. Our framework excels across key biometric metrics, including the Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), and Equal Error Rate (EER). This work sets a new benchmark in biometric morph generation, offering a cutting-edge and efficient solution to enhance biometric security systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 38 of its 67 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

38 of 67 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models27 Jun

Other references · 37

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  7. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  8. 2022Generative Adversarial Networkscited by 5,627
  9. 2014Going Deeper with Convolutionscited by 4,241
  10. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  11. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  12. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
25 more
  1. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  2. 2021Diffusion Autoencoders: Toward a Meaningful and Decodable Representationcited by 449
  3. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  4. 2022Wavelet Diffusion Models are fast and scalable Image Generatorscited by 117
  5. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  6. 2023DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphingcited by 67
  7. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 64
  8. 2023WaveDM: Wavelet-Based Diffusion Models for Image Restorationcited by 47
  9. 2020Deep Face Representations for Differential Morphing Attack Detectioncited by 46
  10. 2020Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detectioncited by 46
  11. 2020Morphing Attack Detection -- Database, Evaluation Platform and Benchmarkingcited by 38
  12. 2020Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networkscited by 36
  13. 2022Are GAN-based Morphs Threatening Face Recognition?cited by 28
  14. 2021ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generationcited by 21
  15. 2023Leveraging Diffusion For Strong and High Quality Face Morphing Attackscited by 14
  16. 2025Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformercited by 14
  17. 2022Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?cited by 13
  18. 2024Diffusion-driven GAN Inversion for Multi-Modal Face Image Generationcited by 12
  19. 2022On the detection of morphing attacks generated by GANscited by 9
  20. 2023Fast-DiM: Towards Fast Diffusion Morphscited by 6
  21. 2023Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Studycited by 5
  22. 2021Adversarially Perturbed Wavelet-based Morphed Face Generationcited by 4
  23. 2023Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detectioncited by 4
  24. 2022Landmark Enforcement and Style Manipulation for Generative Morphingcited by 1
  25. 2024Boosting Unconstrained Face Recognition with Targeted Style Adversarycited by 1

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  2. 2025StableMorph: High-Quality Face Morph Generation with Stable Diffusion
  3. 2026Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models
  4. 2024LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
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  6. 2025Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion
  7. 2026SCHIGAND: A Synthetic Facial Generation Mode Pipeline
  8. 2024MorCode: Face Morphing Attack Generation using Generative Codebooks