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Source-Free Domain Adaptation with Contrastive Domain Alignment and Self-supervised Exploration for Face Anti-spoofing

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Despite promising success in intra-dataset tests, existing face anti-spoofing (FAS) methods suffer from poor generalization ability under domain shift. This problem can be solved by aligning source and target data. However, due to privacy and security concerns of human faces, source data are usually inaccessible during adaptation for practical deployment, where only a pre-trained source model and unlabeled target data are available. In this paper, we propose a novel Source-free Domain Adaptation framework for Face Anti-Spoofing, namely SDA-FAS, that addresses the problems of source knowledge adaptation and target data exploration under the source-free setting. For source knowledge adaptation, we present novel strategies to realize self-training and domain alignment. We develop a contrastive domain alignment module to align conditional distribution across different domains by aggregating the features of fake and real faces separately. We demonstrate in theory that the pre-trained source model is equivalent to the source data as source prototypes for supervised contrastive learning in domain alignment. The source-oriented regularization is also introduced into self-training to alleviate the self-biasing problem. For target data exploration, self-supervised learning is employed with specified patch shuffle data augmentation to explore intrinsic spoofing features for unseen attack types. To our best knowledge, SDA-FAS is the first attempt that jointly optimizes the source-adapted knowledge and target self-supervised exploration for FAS. Extensive experiments on thirteen cross-dataset testing scenarios show that the proposed framework outperforms the state-of-the-art methods by a large margin.

Y. Liu, Y. Chen—Equal contribution. Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc. Datasets were downloaded and evaluated by Shanghai Jiao Tong University researchers.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grants 61932022, 61931023, 61971285, 62120106007, and in part by the Program of Shanghai Science and Technology Innovation Project under Grant 20511100100.

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Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yuchen Liu & Hongkai Xiong

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yabo Chen & Wenrui Dai

  3. Qualcomm AI Research, Shanghai, China

    Mengran Gou & Chun-Ting Huang

Authors
  1. Yuchen Liu
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  2. Yabo Chen
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  3. Wenrui Dai
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  6. Hongkai Xiong
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Corresponding author

Correspondence to Wenrui Dai .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Liu, Y., Chen, Y., Dai, W., Gou, M., Huang, CT., Xiong, H. (2022). Source-Free Domain Adaptation with Contrastive Domain Alignment and Self-supervised Exploration for Face Anti-spoofing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham. https://doi.org/10.1007/978-3-031-19775-8_30

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  • DOI: https://doi.org/10.1007/978-3-031-19775-8_30

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