Official repository of the paper Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Overview

SMDD-Synthetic-Face-Morphing-Attack-Detection-Development-dataset

Official repository of the paper Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

grafik

Paper available under this LINK

grafik

The training data split of the SMDD data can be downloaded from this LINK (please share your name, affiliation, and official email in the request form).

The testing data split of the SMDD data can be downloaded from: (to be uploaded)

The pretrained weight of MixFaceNet-MAD model on SMDD training data can be downloaded from this LINK (please share your name, affiliation, and official email in the request form).

Data preparation

Our face data is preprocessed by the face detection and cropping. The implementation can be found in image_preprocess.py file. Moreover, for further training and test, the corresponding CSV files should be generated. The format of the dataset CSV file in our case is:

image_path,label
/image_dir/image_file_1.png, bonafide
/image_dir/image_file_2.png, bonafide
/image_dir/image_file_3.png, attack
/image_dir/image_file_4.png, attack

Experiment

The main.py file can be used for training and test:

  1. When training and test:
    python main.py \
      --train_csv_path 'train.csv' \
      --test_csv_path 'test.csv' \
      --model_path 'mixfacenet_SMDD.pth' \
      --is_train True \
      --is_test True \
      --output_dir 'output' \
    
  2. When test by using pretrained weight, first download the model and give the model path:
    python main.py \
      --test_csv_path 'test.csv' \
      --model_path 'mixfacenet_SMDD.pth' \
      --is_train False \
      --is_test True \
      --output_dir 'output' \
    

More detailed information can be found in main.py.

Citation:

If you use SMDD dataset, please cite the following paper:

@article{SMDD,
  author    = {Naser Damer and
               C{\'{e}}sar Augusto Fontanillo L{\'{o}}pez and
               Meiling Fang and
               No{\'{e}}mie Spiller and
               Minh Vu Pham and
               Fadi Boutros},
  title     = {Privacy-friendly Synthetic Data for the Development of Face Morphing
               Attack Detectors},
  journal   = {CoRR},
  volume    = {abs/2203.06691},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2203.06691},
  doi       = {10.48550/arXiv.2203.06691},
  eprinttype = {arXiv},
  eprint    = {2203.06691},
}

If you use the MixFaceNet-MAD, please cite the paper above and the original MixFaceNet paper (repo, paper):

@inproceedings{mixfacenet,
  author    = {Fadi Boutros and
               Naser Damer and
               Meiling Fang and
               Florian Kirchbuchner and
               Arjan Kuijper},
  title     = {MixFaceNets: Extremely Efficient Face Recognition Networks},
  booktitle = {International {IEEE} Joint Conference on Biometrics, {IJCB} 2021,
               Shenzhen, China, August 4-7, 2021},
  pages     = {1--8},
  publisher = {{IEEE}},
  year      = {2021},
  url       = {https://doi.org/10.1109/IJCB52358.2021.9484374},
  doi       = {10.1109/IJCB52358.2021.9484374},
}

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.

Image morphing without reference points by applying warp maps and optimizing over them.

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

Alex K 331 May 19, 2022
Privacy as Code for DSAR Orchestration: Privacy Request automation to fulfill GDPR, CCPA, and LGPD data subject requests.

Meet Fidesops: Privacy as Code for DSAR Orchestration A part of the greater Fides ecosystem. ⚡ Overview Fidesops (fee-dez-äps, combination of the Lati

Ethyca 38 May 18, 2022
Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.

The Face Synthetics dataset Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels. It was introduced in ou

Microsoft 458 May 25, 2022
Project code for weakly supervised 3D object detectors using wide-baseline multi-view traffic camera data: WIBAM.

WIBAM (Work in progress) Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data 3D object dete

Matthew Howe 9 Apr 25, 2022
A light weight data augmentation tool for training CNNs and Viola Jones detectors

hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six

Jaiyam Sharma 2 Nov 23, 2019
《Train in Germany, Test in The USA: Making 3D Object Detectors Generalize》(CVPR 2020)

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize This paper has been accpeted by Conference on Computer Vision and Pattern Rec

Xiangyu Chen 87 May 17, 2022
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

MIC-DKFZ 1.1k May 24, 2022
Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)

DSIG Deep Structured Instance Graph for Distilling Object Detectors Authors: Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, Jiaya Jia. [pdf] [slide]

DV Lab 28 Mar 23, 2022
Focal and Global Knowledge Distillation for Detectors

FGD Paper: Focal and Global Knowledge Distillation for Detectors Install MMDetection and MS COCO2017 Our codes are based on MMDetection. Please follow

Mesopotamia 167 May 29, 2022
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral) This is the official implementat

Yifan Zhang 123 May 24, 2022
Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet

Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet, CVPR2021 安全AI挑战者计划第六期:ImageNet无限制对抗攻击 决赛第四名(team name: Advers)

null 24 Mar 27, 2022
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2:ImageNet无限制对抗攻击 介绍 : 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

null 23 May 7, 2022
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition This is the research repository for Vid2

Future Interfaces Group (CMU) 23 Mar 17, 2022
Official implementation of the paper: "LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech"

LDNet Author: Wen-Chin Huang (Nagoya University) Email: [email protected] This is the official implementation of the paper "LDNet

Wen-Chin Huang (unilight) 31 May 9, 2022
A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

SRI-AIC 1 Nov 18, 2021
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Feb 26, 2022
Code and data of the ACL 2021 paper: Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

MetaAdaptRank This repository provides the implementation of meta-learning to reweight synthetic weak supervision data described in the paper Few-Shot

THUNLP 4 Sep 7, 2021
Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

null 57 May 5, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

null 24 May 17, 2022