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A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes
Yiyu Chen,Atnafu, Ayalneh Dessalegn,Schlattner, Isabella,Weldtsadik, Wendimagegn Tariku,Myung-Cheol Roh,Hyoung Joong Kim,Seong-Whan Lee,Blankertz, Benjamin,Fazli, Siamac IEEE 2016 IEEE transactions on information forensics and sec Vol.11 No.12
<P>Lately, electroencephalography (EEG)-based authentication has received considerable attention from the scientific community. However, the limited usability of wet EEG electrodes as well as low accuracy for large numbers of users have so far prevented this new technology to become commonplace. In this study a novel EEG-based authentication system is presented, which is based on the rapid serial visual presentation paradigm and uses a knowledge-based approach for authentication. Twenty-nine subjects' data were recorded and analyzed with wet EEG electrodes as well as dry ones. A true acceptance rate of 100% can be reached for all subjects with an average required login time of 13.5 s for wet and 27 s for dry electrodes. Average false acceptance rates for the dry electrode setup were estimated to be 3.33 x 10(-5).</P>
Copy-Paste 영상 위조의 하이브리드 검출 알고리즘
최용수(YongSoo Choi),Ayalneh Dessalegn Atnafu,이달호(DalHo Lee) 한국디지털콘텐츠학회 2015 한국디지털콘텐츠학회논문지 Vol.16 No.3
Digital image provides many conveniences at the internet environment recently. A great number of applications, like Digital Library, Stock Image, Personal Image and Important Information, require the use of digital image. However, it has fatal defect which is easy to be modified because digital image is only electronic file. Numerous digital image editing software. Copy-Move forgery is the simplest type of forgery that involves copying portion of an image and paste it on different location within the image. There are many approaches to detect Coyp-Move forgery, but all of them have their own limitations. In this paper, visual and invisible feature based forgery detection techniques are tested and analyzed. The analysis shows that pros and cons of these two techniques compensate each other. Therefore, a hybrid of visual based and invisible feature based forgery detection that combine the merits of both techniques is proposed. The experimental results show that the proposed algorithm has enhanced performance compared to individual techniques. Moreover, it provides more information about the forgery, like identifying copy and duplicate regions.