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On Disentangling Spoof Trace for Generic Face Anti-spoofing

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12363)

Abstract

Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed “spoof trace", e.g., color distortion, 3D mask edge, Moiré pattern, and many others. Designing a generic anti-spoofing model to estimate those spoof traces can improve both generalization and interpretability. Yet, this is a challenging task due to the diversity of spoof types and the lack of ground truth. This work designs a novel adversarial learning framework to disentangle the spoof traces from input faces as a hierarchical combination of patterns. With the disentangled spoof traces, we unveil the live counterpart from spoof face, and synthesize realistic new spoof faces after a proper geometric correction. Our method demonstrates superior spoof detection performance on both seen and unseen spoof scenarios while providing visually-convincing estimation of spoof traces. Code is available at https://github.com/yaojieliu/ECCV20-STDN.

Notes

Acknowledgment

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Supplementary material

504473_1_En_24_MOESM1_ESM.pdf (273 kb)
Supplementary material 1 (pdf 273 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Michigan State UniversityEast LansingUSA

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