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SCPAD: An approach to explore optical characteristics for robust static presentation attack detection

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Abstract

Presentation attack detection approaches have achieved great progress on various attack types while adversarial learning technology has become a new threat to these approaches. Now few works are devoted to developing a robust detection method for both physical spoofing faces and digital adversarial faces. In this paper, we find that fake face images from printed photos and replayed videos have a different optical characteristic from the real ones, and the adversarial samples generated by various attacking methods retain this characteristic. By exploring this characteristic, we propose the Spectral Characteristic Presentation Attack Detection (SCPAD), a new approach that detects presentation attacks by reconstructing the color space of input images, which also performs well on adversarial samples. More specifically, a new HSCbb color space is manually constructed by studying the difference in albedo intensity between real faces and fake faces. Then the difference between real and spoofing faces can be effectively magnified and modeled by color texture features with the shallow convolutional network. The experimental results show that our proposed method consistently outperforms the state-of-the-art methods on adversarial faces and also achieves competitive performance on fake faces.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an, 710072, Shaanxi, China

    Chen Dang, Zhaoqiang Xia & Jie Gao

  2. China Academy of Launch Vehicle Technology, 1 Nandahongmen Road, Fengtai, 100076, Beijing, China

    Jing Dai

  3. School of Computer Science and Technology, Shandong University of Finance and Economics, 7366 Erhuan West Road, Jinan, 250014, Shandong, China

    Lei Li

  4. Research & Development Institute of Northwestern Polytechnical University in Shenzhen, 45 Gaoxinnanjiu Road, Shenzhen, 518057, Guangdong, China

    Xiaoyi Feng

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  1. Chen Dang
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  2. Zhaoqiang Xia
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  4. Jie Gao
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  5. Lei Li
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  6. Xiaoyi Feng
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Correspondence to Zhaoqiang Xia or Xiaoyi Feng.

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Conflicts of interest

This work was supported by the National Natural Science Foundation of China (No. No.62002199), the Natural Science Foundation of Shandong Province (No.ZR2020QF109), the Key Research and Development Program of Shaanxi (Nos. 2021ZDLGY15-01, 2021ZDLGY09-04, and 2021GY-004), and Shenzhen Science and Techonlogy Program (No. GJHZ20200731095204013)

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Dang, C., Xia, Z., Dai, J. et al. SCPAD: An approach to explore optical characteristics for robust static presentation attack detection. Multimed Tools Appl 83, 14503–14520 (2024). https://doi.org/10.1007/s11042-023-15870-4

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