Face Presentation Attack Detection
17 papers with code • 2 benchmarks • 5 datasets
Latest papers with no code
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems
We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting.
Does complimentary information from multispectral imaging improve face presentation attack detection?
We present PAD based on multispectral images constructed for eight different presentation artifacts resulted from three different artifact species.
PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics
Presentation Attack Detection (PAD) is a crucial stage in facial recognition systems to avoid leakage of personal information or spoofing of identity to entities.
Sound-Print: Generalised Face Presentation Attack Detection using Deep Representation of Sound Echoes
In this paper, we present an acoustic echo-based face Presentation Attack Detection (PAD) on a smartphone in which the PAs are detected based on the reflection profiles of the transmitted signal.
Saliency-based Video Summarization for Face Anti-spoofing
Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency.
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
The availability of handy multi-modal (i. e., RGB-D) sensors has brought about a surge of face anti-spoofing research.
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning.
Surveillance Face Presentation Attack Detection Challenge
Based on this dataset and protocol-$3$ for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios.
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation
Extensive experimentation with eight research datasets (rPPG-specific: DDPM, CDDPM, PURE, UBFC, ARPM; deep fakes: DFDC; face presentation attack detection: HKBU-MARs; rPPG outlier: KITTI) show better accuracy of anomaly detection for deep learning models incorporating the proposed training (75. 8%), compared to models trained regularly (73. 7%) and to hand-crafted rPPG methods (52-62%).
PDVN: A Patch-based Dual-view Network for Face Liveness Detection using Light Field Focal Stack
A patch-based dual-view network (PDVN) is proposed in this paper to leverage the merits of LFFS for face presentation attack detection (PAD).