Presentation Attack Detection

Overview

This repository is dedicated to the image-based Presentation Attack Detection - PAD - systems in two different domains: (i) cork and (ii) face PAD. The proposed PAD system relies on the combination of two different color spaces and uses only a single frame to distinguish from a bona fide image and an image attack, see Fig. 1.

Fig. 1 - General flowchart for the developed image-based PAD system.

Contents

• Trained models for the public face anti-spoofing Print-attack database;
• Trained models for the public face anti-spoofing Replay-attack database;
• Error rate curve for the development set of the Print-attack database
• Error rate curve for the development set of the Replay-Attack database;
• [1] - Image-based Object Spoofing Detection - Conference paper

Results

Method	Print-attack		Replay-attack
	EER(%)	HTER(%)	EER(%)	HTER(%)
YCRCB+LUV+ETC [1]	1.33	0.00	0.00756	0.5954
YCRCB+LUV+SVM [1]	0.00	1.76	4.30	7.86

Cork Spoofing Detection

Face Spoofing Detection

Demonstrative results of the proposed face PAD system - YCRCB+LUV+ETC. The classification model used in this test was trained using the training set of the Replay-Attack database.

How to cite

If you use any part of this work please cite [1]:

@InProceedings{10.1007/978-3-030-05288-1_15,
author="Costa, Valter
and Sousa, Armando
and 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"
}