Official repository of the paper MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders
*** Accepted at the 11th International Workshop on Biometrics and Forensics 2023 (IWBF 2023) ***
Paper available under this LINK
The benchmark SYN-MAD 2022 data is available under: REPO+DATA + PAPER
Morphing script is provided.
The MorDIFF data can be downloaded from this LINK (please share your name, affiliation, and official email in the request form).
Citation:
If you use MorDIFF datas, please cite the following paper:
@misc{MorDIFF,
doi = {10.48550/ARXIV.2302.01843},
url = {https://arxiv.org/abs/2302.01843},
author = {Damer, Naser and Fang, Meiling and Siebke, Patrick and Kolf,
Jan Niklas and Huber, Marco and Boutros, Fadi},
title = {MorDIFF: Recognition Vulnerability and Attack Detectability of Face
Morphing Attacks Created by Diffusion Autoencoders},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
and the base benchmark, the SYN-MAD 2022 paper:
@inproceedings{SYNMAD2022,
author = {Marco Huber and
Fadi Boutros and
Anh Thi Luu and
Kiran B. Raja and
Raghavendra Ramachandra and
Naser Damer and
Pedro C. Neto and
Tiago Gon{\c{c}}alves and
Ana F. Sequeira and
Jaime S. Cardoso and
Jo{\~{a}}o Tremo{\c{c}}o and
Miguel Louren{\c{c}}o and
Sergio Serra and
Eduardo Cerme{\~{n}}o and
Marija Ivanovska and
Borut Batagelj and
Andrej Kronovsek and
Peter Peer and
Vitomir Struc},
title = {{SYN-MAD} 2022: Competition on Face Morphing Attack Detection Based
on Privacy-aware Synthetic Training Data},
booktitle = {{IEEE} International Joint Conference on Biometrics, {IJCB} 2022,
Abu Dhabi, United Arab Emirates, October 10-13, 2022},
pages = {1--10},
publisher = {{IEEE}},
year = {2022},
url = {https://doi.org/10.1109/IJCB54206.2022.10007950},
doi = {10.1109/IJCB54206.2022.10007950},
}
To use the provided morphing script, you need to download the pre-trained model from Preechakul et al. REPO. If you use the provided morphing script with the pre-trained model provided by Preechakul et al. (paper) please additionally follow their citation requierments (REPO).
The MorDIFF data, such as the SYN-MAD 2022 benchmark, is based on the Face Research Lab London dataset (FRLL). Please follow all the requierments of the FRLL dataset.
License:
The dataset, the implementation, or trained models, use is restricted to research purpuses. The use of the dataset or the implementation/trained models for product development or product competetions (incl. NIST FRVT MORPH) is not allowed. This project is licensed under the terms of the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Copyright (c) 2020 Fraunhofer Institute for Computer Graphics Research IGD Darmstadt.