  Electrical network frequency (ENF) is the signature of a power distribution
grid which represents the nominal frequency (50 or 60 Hz) of a power system
network. Due to load variations in a power grid, ENF sequences experience
fluctuations. These ENF variations are inherently located in a multimedia
signal which is recorded close to the grid or directly from the mains power
line. Therefore, a multimedia recording can be localized by analyzing the ENF
sequences of that signal in absence of the concurrent power signal. In this
paper, a novel approach to analyze location forensics using ENF sequences
extracted from a number of power and audio recordings is proposed. The digital
recordings are collected from different grid locations around the world.
Potential feature components are determined from the ENF sequences. Then, a
multi-class support vector machine (SVM) classification model is developed to
validate the location authenticity of the recordings. The performance
assessments affirm the efficacy of the presented work.
