List of Information Forensics and Security IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Information Forensics and Security for the year 2015

1. Information Forensics and Security IEEE 2015 Projects
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List Link : http://kasanpro.com/projects-list/information-forensics-and-security-ieee-2015-projects
Title :Cardiovascular Biometrics: Combining Mechanical and Electrical Signals
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/cardiovascular-biometrics-combining-mechanical-electrical-signals
Abstract : The electrical signal originating from the heart, the electrocardiogram (ECG), has been examined for its
potential use as a biometric. Recent ECG studies have shown that an inter-session authentication performance below
6% equal error rate (EER) can be achieved using training data from two days while testing with data from a third day.
More recently, a mechanical measurement of cardiovascular activity, the laser Doppler vibrometry (LDV) signal, was
proposed by our group as a biometric trait. The inter-session authentication performance of the LDV biometric system
is comparable to that of the ECG biometric system. Combining both the electrical and mechanical aspects of the
cardiovascular system, an overall improvement in authentication performance can be attained. In particular, the
multibiometric system achieves about 2% EER. Moreover, in the identification mode, with a testing database
containing 200 individuals, the rank-1 accuracy improves from about 80% for each individual biometric system, to
about 92% for the multibiometric system. Although there are implementation issues that would need to be resolved
before this combined method could be applied in the field, this report establishes the basis and utility of the method in
principle, and it identifies effective signal analysis approaches.
Title :Live Video Forensics: Source Identification in Lossy Wireless Networks
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/source-identification-lossy-wireless-networks
Abstract : Video source identification is very important in validating video evidence, tracking down video piracy
crimes, and regulating individual video sources. With the prevalence of wireless communication, wireless video
cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However,
wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless
transmissions. The existing source identification methods experience significant performance degradation or even fail
to work when identifying videos with blocking and blurring. In this paper, we propose a method that is effective and
efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel
signatures and selective frame processing into source identification, which significantly improve the identification
speed. We conduct extensive realworld experiments to validate our method. The results show that the source
identification accuracy of the proposed scheme largely outperforms the existing methods in the presence of video
blocking and blurring. Moreover, our method is able to identify the video source in a near-real-time fashion, which can
be used to detect the wireless camera spoofing attack.