# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 1 Introduction  
Digital multimedia materials, that is, audio, image, and video recordings, contain vast amounts of information which can be exploited in forensic investigations. In light of how digital manipulation techniques are always developing and how they may have far-reaching effects on different spheres of society and the economy, the field of digital forensics has seen an increasing growth in recent decades. In order to combat multimedia forgeries and ensure the authenticity of multimedia material, researchers have focused on developing new methods in the field of digital forensics. Electric network frequency (ENF) has been utilized in recent years as a tool in forensic applications. Analysis of the ENF is a forensic tool used to verify the authenticity of multimedia recordings and spot any attempts at manipulation [1; 2; 3; 4]. The ENF is the supply frequency of an electric power grid and varies in time around its nominal value of 60 Hz in North America and 50 Hz in Europe, Australia, and much of the rest of the world as a result of inconsistencies between power network supply and demand [1; 5]. The nature of these inconsistencies can be observed to be random, unique per time, and usually quite the same across all locations connected by the same power grid. Consequently, an ENF signal recorded at any location in time, connected to a certain mains power can serve as a reference ENF signal for the entire region serviced by that power network for that period of time [6; 7]. The ENF's fluctuating/instantaneous values over time is considered as an ENF signal. An ENF signal is embedded in audio files created with devices connected to the mainspower or located in environments where electromagnetic interference or acoustic chains hum is present [1; 7; 8]. This ENF signal can be estimated from the recordings using time-domain or frequency-domain techniques and utilized for various forensic and anti-forensic applications, such as time-stamp verification [5; 8; 9], audio/video authentication [10; 11], location of recording estimation [12; 13; 14], power grid identification [15; 16; 17; 18; 19; 20], and estimation of camera read-out time [21]. New studies have found that ENF analysis may be employed in other areas of multimedia signal processing, such as in audio and video record synchronization [22], historical audio recording alignment [23], and video synchronization without overlapping scenes [22]. Studies have recently shown that ENF signals can also be extracted from video recordings made under the illumination of a light source powered by a mains grid [5; 24; 25; 26; 27]. Fluorescent lights and incandescent bulbs used in indoor lighting fluctuate in light intensity at double the supply frequency, causing a nearly impossible to notice flickering that occurs in the illuminated environment. As a result, videos captured under indoor illumination settings using a camera may contain ENF signals. However most commonly/widely used cameras sensors do not capture light in the same manner. Charge-coupled device (CCD) sensors commonly associated with global shutter mechanisms capture all the pixels in a video frame at the same time/instant. Unlike CCDs, complementary metal oxide semiconductor (CMOS) sensors often have a rolling shutter mechanism that causes the sensor to scan the rows of each frame sequentially so that various rows of the same frame are exposed at slightly different instants [26; 28]. One of the main issues confronted in the extraction of the embedded ENF signals from video recordings (particularly for videos captured with CCD cameras) is the problem of aliasing to DC due to insufficient/inadequate sampling rates. However, the authors of [25; 29] demonstrated that the rolling shutter mechanism, although considered traditionally detrimental to the analysis of image and video, could be exploited to enhance the effective sampling rate for ENF extraction from video recording.