Competition on counter measures to 2-D facial spoofing attacks

By Murali Mohan Chakka, André Anjos, Sébastien Marcel, Roberto Tronci, Daniele Muntoni, Gianluca Fadda, Maurizio Pili, Nicola Sirena, G. Murgia, Marco Ristori and 14 more. 2011

178 citations recorded by the source corpus.

Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems (2016, methodology): “Pictures were taken with cooperation (Print [26], [28] and Photo Attack datasets [17]) and without cooperation of the targeted client (Personal Photo Attack [17]).”
Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “Consequently, there are numerous studies [1,2] and open challenges [3,4] on anti-spoofing techniques assessing the spoofing detector's ability to distinguish between genuine and fake attempts for especially these two traits.”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, methodology): “From the six previous databases, the REPLAY-ATTACK DB is probably the most significant one, not only for its size, multiple and well defined protocols and attacks covered, but also because it was used in the last edition of the Competition on Countermeasures to 2D Facial Spoofing Attacks held in 201”
Face Spoofing Detection Using Colour Texture Analysis (2016, background): “Therefore, it is not surprising that fusion of several methods analysing the motion and facial appearance has been a common trend in the recently organized two competitions on software-based antispoofing [31], [32].”
Security evaluation of biometric authentication systems under real spoofing attacks (2012, methodology): “…and reduction in the cost of high quality video cameras and portable devices have also made it easy to obtain or capture genuine user’s facial video samples without subject’s consent and awareness, which can be later presented to the system using a portable device for spooﬁng purpose [34, 6, 21].”
LBP − T OP based countermeasure against face spooﬁng attacks (2012, background): “1 [1-2] [1-3] [1-4] [1-5] Rt 5 10 15 20 25 30 35”
Learn Convolutional Neural Network for Face Anti-Spoofing (2014, background): “In [4], the team UOULU exploited background region for feature extraction, and achieved best performance in the competition.”
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge (2015, background): “The research is now relatively mature, with several competitive evaluations having been organised for various modalities including face [2], fingerprint [3] and iris [4] recognition.”
Evaluation of serial and parallel multibiometric systems under spoofing attacks (2012, methodology): “Some works have also shown that iris and face verification systems may be fooled by using suitable contact lenses [9], and simple photographs [8] of a genuine user.”
Face Anti-spoofing: Visual Approach (2014, methodology): “Fusion of different antispoofing techniques has not been studied much besides the algorithms [47–49] proposed within the context of the IJCB 2011 competition on countermeasures to 2D facial spoofing attacks [18].”
Presentation Attack Detection for Face Recognition Using Light Field Camera (2015, background): “The feasibility of these attacks on face recognition systems are acknowledged by the number of recent publications in this field [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], the organization of competitions [14], [15] and the evolution of standards [16] that show the strong i”
Biometrics Evaluation Under Spoofing Attacks (2014, background): “6 Concerning the face anti-spooﬁng systems, they can be categorized in three groups with respect to the cues they use to detect the spooﬁng attack [43].”
Spoofing Face Recognition With 3D Masks (2014, background): “The second group of methods aims to detect spoofing attacks by analysing the motion in the scene based on the fact that planar objects like a sheet of paper or a mobile phone screen move in a significantly different way compared to real faces.”
Motion-based counter-measures to photo attacks in face recognition (2014, background): “To explore different types of attacks without modifying other dataset parameters, we now introduce the PHOTO-ATTACK database, which enriches the PRINT-ATTACK database with mobile phone and high-resolution screen attacks.”
Face Recognition on Consumer Devices: Reflections on Replay Attacks (2015, background): “There has been a significant focus on solving the problem of presenting fake biometrics (spoofing attacks) to sensors [7]–[9], [11], [13], [33], [34].”