Are GAN-based Morphs Threatening Face Recognition?

This package contains the source code for generating the morphing attacks used in the experiments of the paper "Are GAN-based Morphs Threatening Face Recognition?":

@INPROCEEDINGS{Sarkar_ICASSP_2022,
    author    = {Sarkar, Eklavya and Korshunov, Pavel and Colbois, Laurent and Marcel, S{\'e}bastien},
    projects  = {Idiap, Biometrics Center},
    title     = {Are GAN-based Morphs Threatening Face Recognition?},
    booktitle = {International Conference on Acoustics, Speech, & Signal Processing (ICASSP 2022)},
    year      = {2022},
    note      = {Accepted for Publication in ICASSP2022},
    pdf       = {http://publications.idiap.ch/attachments/papers/2022/Sarkar_ICASSP_2022.pdf}
}

Installation

This package is part of the signal-processing and machine learning toolbox bob. Install conda before continuing.

Download the source code of this paper and unpack it. Then, you can create and activate the required conda environment with the following commands:

$ cd bob.paper.icassp2022_morph_generate
$ conda install -n base -c conda-forge mamba
$ mamba env create -f environment.yml -n bob.paper.icassp2022_morph_generate
$ conda activate bob.paper.icassp2022_morph_generate

This will install all the required software to generate the morphing attacks.

Downloading Models

The projection process relies on several pre-existing models:

• DLIB Face Landmark detector for cropping and aligning the projected faces exactly as in FFHQ. Example.
• StyleGAN2 as the main face synthesis network. We fork the official repository. The Config-F, trained on FFHQ at resolution 1024 x 1024, is employed.
• A pretrained VGG16 model, used to compute a perceptual loss between projected and target image.

In order to download those models, one must specify the destination path of choice in the ~/.bobrc file, through the following commands:

$ bob config set sg2_morph.dlib_lmd_path /path/to/dlib/landmark/detector.dat
$ bob config set sg2_morph.sg2_path /path/to/stylegan2/pretrained/model.pkl
$ bob config set sg2_morph.vgg16_path /path/to/vgg16/pretrained/model.pkl

Finally, all the models can be downloaded by running:

$ python download_models.py

Generating Morphs

Note: StyleGAN2 requires custom GPU-only operations, and at least 12 GB of GPU RAM. Therefore, to run all following examples and perform additional experiments, it is necessary to run this code on a GPU.

The script options can be viewed with:

$ conda activate bob.paper.icassp2022_morph_generate
$ python gen_morphs.py -h

The morphs of the following types of morphs can be generated at different alphas:

• OpenCV
• FaceMorpher
• StyleGAN2
• MIPGAN-II

Typical usage:

$ python gen_morphs.py --opencv --facemorpher --stylegan2 --mipgan2 -s path/to/folder/of/images/ -l path/to/csv/of/pairs.csv -d path/to/destination/folder --latents path/to/latent/vectors --alphas 0.3 0.5 0.7

The pairs.csv file should simply be a 2 column .csv file without a header containing only the filenames of the 2 images you want to morph:

• image1.png, image2.png
• image1.png, image3.png

Note: Keep in mind that for the --stylegan2 and --mipgan2 arguments, it is necessary to have the latent vectors of all required images generated beforehand.

This can be done with the gen_latents.py. Typical usage:

$ python gen_latents.py -s path/to/folder/of/images/

License

This package uses some components from the official release of the StyleGAN2 model, which is itself released under the Nvidia Source Code License-NC, as well as from OpenCV and Facemorpher repositories, both of which are released under a "Non-Commercial Research and Educational Use Only" license.

Contact

For questions or reporting issues to this software package, kindly contact the first author.