Forensic Research Using Grid Data
Digital audio and video recordings often capture the power system “hum” as low-level background noise. This signature can be used as a unique fingerprint to determine if a recording was made at a particular place and time . The basic approach is to extract the 50/60Hz electric network frequency (or its harmonics) from the digital audio recordings and compare it against a reliable reference frequency database (here FNET database is used). The “hum” not only exists in digital recordings whose recording device is directly connected to the electrical network but also in recordings whose recording device is battery-powered. A study was performed to better understand how grid signals find their way onto battery-powered recordings . Figure 1 shows the harmonics (i.e. 120 Hz, 180 Hz, 240 Hz, etc.) of 60 Hz (electric network frequency) that exist in the audio recordings of battery-powered cell phones. Methodologies have been developed  that can be used to accurately and reliably extract ENF data from digital recordings. A comparison between extracted Electric Network Frequency (ENF) and FNET/GridEye data is shown in Figure 2. We have also worked on ways of matching the extracted ENF to the FNET database to detect tampering . The frequency and phase angle will show sudden changes if something has tampered with the recording. Figure 3 illustrates the frequency change of different lengths of deletion. In addition, a Graphical User Interface (GUI) has been developed that will allow easy use of the authentication procedure by law enforcement officials.
 C. Grigoras, “Applications of ENF Analysis in Forensic Authentication of Digital Audio and Video Recordings,” Journal of the Audio Engineering Society, vol. 57, no. 9, pp. 643-661, 2009.
 J. Chai, F. Liu, Z. Yuan, R. W. Conners and Y. Liu, “Source of ENF in Battery-powered Digital Recordings,” The Audio Engineering Society 135th International Convention, New York City, USA, 2013.
 Y. Liu, Z. Yuan, P. N. Markham, R. W. Conners, and Y. Liu, “Application of Power System Frequency for Digital Audio Authentication,” IEEE Trans. Power Delivery, vol. 27, no. 4, pp. 1820-1828, 2012.
 J. Chai, Y. Liu, Z. Yuan, R. W. Conners and Y. Liu, “Tampering Detection of Digital Recordings using Electric Network Frequency and Phase Angle,” The Audio Engineering Society 135th International Convention, New York City, USA, 2013.