Backdoored Face-Networks (BFN) dataset
Description
This database is a database of backdoored neural networks intended for face recognition. The networks are of the FaceNet architecture and are trained on Casia-WebFace, with and without additional samples (which are the source of the backdoor). More information regarding backdoors and the project within which this fits can be found in the public release of the source code : https://gitlab.idiap.ch/bob/bob.paper.backdoored_facenets.biosig2022.
There are two sets of backdoored networks. A first one with backdoors with varying triggers (the triggers dataset) and a second one with backdoors with varying trigger placement strategies (the locations dataset). A third set of networks is also provided, just regular networks without any backdoor, referred to as the clean dataset. Configuration yaml files are provided to replicate the backdoored networks using the repository content linked above, in addition to pickle files containing validation scores on all validation datasets.
Reference
If you use this dataset, please cite the following publication:
«An anomaly detection approach for backdoored neural networks: face recognition as a case study», by Alexander Unnervik and Sébastien Marcel, published at 21st International Conference of the Biometrics Special Interest Group (BIOSIG 2022)