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We report a multi-harmonic histogram method for extracting and analyzing electric network frequency (ENF) signals to identify power grids. Given a voltage-time measurement of a power grid with a base frequency f0, we compute the ENF signals at multiple harmonic locations f_0 and extract (i) a histogram of the magnitudes of the ENF; (ii) a histogram of the signal power and noise power surrounding the ENF; (iii) a histogram of the signal-to noise-ratio (SNR) of the ENF.

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We present two contributions in this work: i)Novel electric network frequency (ENF) classification algorithm, and ii)Circuit for measuring power signals from the power grid.We first propose a novel ENF signal estimation algorithm.This algorithm explicitly makes use of the harmonic information present in the signal and estimates the nominal frequency based on the most reliable harmonic. The ENF signal is estimated from the most reliable harmonic by employing a Gaussian weighting window to mitigate the effects of noise. We

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The Electric network frequency (ENF) signal is a unique signal for different parts of the world. It is captured by electric devices, and can be used in authentication and automatic synchronization of digital media recordings. In this paper we propose an algorithm to extract ENF from power and audio recordings, and use ENF criterion to identify the region-of-recording. We also propose a design of a circuit to record the electrical power grid.

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In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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A. Hajj-Ahmad, S. Baudry, B. Chupeau, G. Do¨err, and M. Wu, “Flicker forensics for camcorder piracy,” published in IEEE Transactions on Information Forensics and Security. Available here: http://doi.org/10.1109/TIFS.2016.2603603

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IEEE Distinguished Lecture on
"Seeing the Invisibles: A Backstage Tour of Information Forensics"

(Given at the School of ICASSP 2015 in April 2015 and IEEE Signal Processing Chapters in Fall 2015)

by Prof. Min Wu
University of Maryland, College Park, USA

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This work studies the paper authentication problem by exploiting optical features through mobile imaging devices to characterize the unique, physically unclonable properties of paper surface. Prior work showing high matching accuracy either used a consumer-level scanner for estimating a projected normal vector field of the surface of the paper as the feature for authentication, or used an industrial camera with controlled lighting to obtain an appearance image of the surface as the feature.

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