• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• 정보과학회 논문지
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• 제42권4호
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• pp.483-486
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• 2015

얼굴 인식기반의 사용자 보안 시스템은 접근이 허가된 사용자의 사진이나 비디오를 이용한 공격에 취약하다는 단점을 가지고 있다. 본 연구에서는 인증되지 않은 사용자가 비디오를 이용하여 시스템에 접근할 경우 해당 공격 시도를 검출하기 위한 위변조(Spoof) 검출 방법을 제안한다. 제안하는 방법은 연속된 3개의 Frame에서 푸리에 변환과 Dense-SIFT 구분자를 사용하여 400개의 실제 및 위변조 비디오 영상을 대상으로 실험한 결과 99%의 검출 정확도를 보였다.