University of Oulu

L. Li, Z. Xia, A. Hadid, X. Jiang, H. Zhang and X. Feng, "Replayed Video Attack Detection Based on Motion Blur Analysis," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 9, pp. 2246-2261, Sept. 2019. doi: 10.1109/TIFS.2019.2895212

Replayed video attack detection based on motion blur analysis

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Author: Li, Lei1; Xia, Zhaoqiang1; Hadid, Abdenour1,2;
Organizations: 1Northwestern Polytechnical University, Xi’an, China
2Center for Wireless Communications, Oulu University, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 27.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019092529736
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-09-25
Description:

Abstract

Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. That is because the replayed fake faces contain a variety of aliveness signals, such as eye blinking and facial expression changes. Replayed video attacks occur when attackers try to invade biometric systems by presenting face videos in front of the cameras, and these videos are often launched by a liquid-crystal display (LCD) screen. Due to the smearing effects and movements of LCD, videos captured from the real and replayed fake faces present different motion blurs, which are reflected mainly in blur intensity variation and blur width. Based on these descriptions, a motion blur analysis-based method is proposed to deal with the replayed video attack problem. We first present a 1D convolutional neural network (CNN) for motion blur intensity variation description in the time domain, which consists of a serial of 1D convolutional and pooling filters. Then, a local similar pattern (LSP) feature is introduced to extract blur width. Finally, features extracted from 1D CNN and LSP are fused to detect the replayed video attacks. Extensive experiments on two standard face PAD databases, i.e., relay-attack and OULU-NPU, indicate that our proposed method based on the motion blur analysis significantly outperforms the state-of-the-art methods and shows excellent generalization capability.

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Series: IEEE transactions on information forensics and security
ISSN: 1556-6013
ISSN-E: 1556-6021
ISSN-L: 1556-6013
Volume: 14
Issue: 9
Pages: 2246 - 2261
DOI: 10.1109/TIFS.2019.2895212
OADOI: https://oadoi.org/10.1109/TIFS.2019.2895212
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This paper is partly supported by the National Natural Science Foundation of China (No.61702419), the Natural Science Basic Research Plan of Shaanxi Province of China(No. 2018JQ6090), and the Fundamental Research Funds for the Central Universities (No.3102015BJ(II)ZS016).
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