[논문]Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor

Ali, Zahid; Park, Unsang

doi:10.3837/tiis.2019.02.022

Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor 원문보기

KSII Transactions on internet and information systems : TIIS, v.13 no.2, 2019년, pp.892 - 911

Ali, Zahid (Department of Computer Science and Engineering, Sogang University) , Park, Unsang (Department of Computer Science and Engineering, Sogang University)

Abstract ▼ AI-Helper

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.

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표/그림 (7)

그림 Fig. 1. (a) Original image, (b), (c), and (d) are blurred images of (a). (e) Original video frame, (g) spoof video frame (warped photo attack) from CASIA-FASD and (f) shows a closer look of (e) and (g). (h), (i), and (j) are the frequency domain equivalent images of (a), (d), and (g), respectively.
그림 Fig. 2. (a) Original Image (b) Warped Photo Attack (c) Cut Photo Attack and (d) Replay Video Attack. (e), (f), (g), and (h) are the frequency domain images of (a), (b), (c), and (d), respectively. Red boxes in the frequency domain images highlight that the characteristic patterns of spoofing attacks are more prominent in certain regions.
그림 Fig. 3. Overall schematic representation of our proposed method
그림 Fig. 4. Classification accuracies of valid and fake videos for each block in a frequency domain image
표 Table 1. Spoofing attack detection performance on CASIA-FASD in EER (%) by the proposed method for different protocols and its comparison with [16], [17], [18], and [20]
표 Table 2. Comparisons of spoofing attack detection performance between the proposed method and state-of-the-art methods on CASIA-FASD, Replay-Attack, 3DMAD and UVAD DB
표 Table 3. Comparison of computational cost

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