Presentation Attack Detection

Overview

Contents

Results

Cork Spoofing Detection

Face Spoofing Detection

How to cite

@InProceedings{10.1007/978-3-030-05288-1_15,author="Costa, Valterand Sousa, Armandoand Reis, Ana",editor="Barneva, Reneta P.and Brimkov, Valentin E.and Tavares, Jo{\~a}o Manuel R.S.",title="Image-Based Object Spoofing Detection",booktitle="Combinatorial Image Analysis",year="2018",publisher="Springer International Publishing",address="Cham",pages="189--201",abstract="Using 2D images in authentication systems raises the question of spoof attacks: is it possible to deceive an authentication system using fake models possessing identical visual properties of the genuine one? In this work, an anti-spoofing method approach for a wine anti-counterfeiting system is presented. The proposed method relies in two different color spaces: CIE L*u*v* and {\$}{\$}YC{\_}rC{\_}b{\$}{\$}, to distinguish between a genuine instance and a spoof attack. To evaluate the proposed strategy, two databases were used: a private database, with photos/2D attacks of cork stoppers, created for this work; and the public Replay-Attack database that is used for face spoofing detection methods testing. The results on the private database show that the anti-spoofing approach is able to distinguish with high accuracy a real photo from an attack. Regarding the public database, the results were obtained with existing methods, as the best HTER results using a single frame approach.",isbn="978-3-030-05288-1"}