Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks
- 30 April 2015
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Forensics and Security
- Vol. 10 (5), 1025-1038
- https://doi.org/10.1109/TIFS.2015.2395139
Abstract
Spoofing attacks or impersonation can be easily accomplished in a facial biometric system wherein users without access privileges attempt to authenticate themselves as valid users, in which an impostor needs only a photograph or a video with facial information of a legitimate user. Even with recent advances in biometrics, information forensics and security, vulnerability of facial biometric systems against spoofing attacks is still an open problem. Even though several methods have been proposed for photo-based spoofing attack detection, attacks performed with videos have been vastly overlooked, which hinders the use of the facial biometric systems in modern applications. In this paper, we present an algorithm for video-based spoofing attack detection through the analysis of global information which is invariant to content, since we discard video contents and analyze content-independent noise signatures present in the video related to the unique acquisition processes. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access videos. For that, we use the Fourier spectrum followed by the computation of video visual rhythms and the extraction of different characterization methods. For evaluation, we consider the novel unicamp video-attack database, which comprises 17 076 videos composed of real access and spoofing attack videos. In addition, we evaluate the proposed method using the replay-attack database, which contains photo-based and video-based face spoofing attacks.Keywords
Funding Information
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior through the DeepEyes
- National Council for Scientific and Technological Development (304352/2012-8, 477457/2013-4, 477662/2013-7, 487529/2013-8)
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2010/05647-4, 2011/22749-8)
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Microsoft
This publication has 33 references indexed in Scilit:
- Video-Based Face Spoofing Detection through Visual Rhythm AnalysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Counter-measures to photo attacks in face recognition: A public database and a baselinePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Vision of the Unseen: Current Trends and Challenges in Digital Image and Video ForensicsACM Computing Surveys, 2011
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A method for cut detection based on visual rhythmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Multiresolution gray-scale and rotation invariant texture classification with local binary patternsIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Fast Text Caption Localization on Video Using Visual RhythmLecture Notes in Computer Science, 2002
- Support-vector networksMachine Learning, 1995
- PLS regression methodsJournal of Chemometrics, 1988
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973