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Using LDP-TOP in Video-Based Spoofing Detection


Quoc Tin Phan, Duc Tien Dang Nguyen, Giulia Boato, Francesco De Natale

Publication Type: 
Book Chapter
Face authentication has been shown to be vulnerable against three main kinds of attacks: print, replay, and 3D mask. Among those, video replay attacks appear more challenging to be detected. There exist in the literature many countermeasures to face spoofing attacks, but a sophisticated detector is still needed to deal with particularly high-quality video based attacks. In this work, we perform analysis on the noise residual in frequency domain, and extract discriminative features by using a dynamic texture descriptor to characterize video based spoofing attacks. We propose a promising detector, which produces competitive results on the most challenging dataset of video based spoofing.
Publication Date: 
Research Group: 
Dublin City University (DCU)
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