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There has been an increasing amount of work surrounding the Electric Network Frequency (ENF) signal, an environmental signature captured by audio and video recordings made in locations where there is electrical activity. ENF is the frequency of power distribution networks, 60 Hz in most of the Americas and 50 Hz in most other parts of the world. The ubiquity of this power signature and the appearance of its traces in media recordings motivated its early application toward time–location authentication of audio recordings. Since then, more work has been done toward utilizing this signature for other forensic applications, such as inferring the grid in which a recording was made, as well as applications beyond forensics, such as temporally synchronizing media pieces. The goal of this chapter is to provide an overview of the research work that has been done on the ENF signal and to provide an outlook for the future.

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Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time-location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence-classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace; if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image's capturing location.

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