SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

Abstract

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legallysensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available 1 .

Attacking Face Recognition With T-Shirts: Database, Vulnerability Assessment, and Detection

“…Slightly better results were achieved when trained on HQ-WMCA and tested on WMCA, achieving an HTER of 18.2%. In [46] the authors introduced a new dataset of PAs based on synthetic face images and demonstrated the feasibility of using synthetic data for PAD.…”