  Information regarding the location of power distribution grid can be
extracted from the power signature embedded in the multimedia signals (e.g.,
audio, video data) recorded near electrical activities. This implicit mechanism
of identifying the origin-of-recording can be a very promising tool for
multimedia forensics and security applications. In this work, we have developed
a novel grid-of-origin identification system from media recording that consists
of a number of support vector machine (SVM) followed by pole-matching (PM)
classifiers. First, we determine the nominal frequency of the grid (50 or 60
Hz) based on the spectral observation. Then an SVM classifier, trained for the
detection of a grid with a particular nominal frequency, narrows down the list
of possible grids on the basis of different discriminating features extracted
from the electric network frequency (ENF) signal. The decision of the SVM
classifier is then passed to the PM classifier that detects the final grid
based on the minimum distance between the estimated poles of test and training
grids. Thus, we start from the problem of classifying grids with different
nominal frequencies and simplify the problem of classification in three stages
based on nominal frequency, SVM and finally using PM classifier. This cascaded
system of classification ensures better accuracy (15.57% higher) compared to
traditional ENF-based SVM classifiers described in the literature.
