# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 1 Introduction
Due to the sequential acquisition of the rows of a frame at distinct time instants, referred to as the read-out time \(T_{ro}\), the rolling shutter can facilitate the extraction of ENF signals from video recordings by raising the effective sample rate by a factor of the number of rows to prevent the aliasing effect. The \(T_{ro}\) is the amount of time it takes the camera to capture the rows of a single frame and it is a camera-specific parameter that can be leveraged to characterize CMOS cameras with rolling shutter mechanisms. The author of [30] proposed a method that can extract a flicker signal and the camera \(T_{ro}\) from a pirated video and utilize them to identify the LCD screen and camera used to create the pirated movie. Owing to the similarities between the flicker signal and the ENF signal, the authors of [21] appropriated the flicker-based technique in an ENF-based approach that can estimate the \(T_{ro}\) value of a camera that creates a video containing an ENF. Their experimental results showed that the approach could estimate the \(T_{ro}\) value with high accuracy. In this work, inspired by [29], we present an approach that exploits the \(T_{ro}\) and the ENF to identify the camera used to record an ENF-containing video. Figure 1 shows the block diagram of our proposed method. Our approach considers a practical scenario where a video obtained from the public, including social media, is being investigated by law enforcement to ascertain if it originated from a suspect's camera. In our proposed approach, the ENF extraction method, adapted from [29], conducts an equal uniform sampling over time by returning zeros during the idle period at the end of every frame period to generate the row signals after certain preprocessing. The row signal/reference signal is passed through Fourier analysis (spectrogram) to generate the ENF traces. The ENF is then extracted by finding the dominant instantaneous frequency within a narrow band around the frequency of interest. Quadratic interpolation was utilized to refine the ENF estimate. This method attains a high signal-to-noise ratio (SNR) at the cost of the need to know the read-out time (\(T_{ro}\)) parameter, which is camera-model-dependent. Figure 2 shows the block diagram for estimating the ENF signal from video frames. The \(T_{ro}\) is a sensitive parameter in the method and is employed to compute the number of zeros to be inserted in the idle period for the specific camera [30] or specific video resolution and frame rate [31] that captured the video before analyzing the video, which is critical in estimating an ENF signal with no distortion. Our proposed approach therefore employs a database of camera \(T_{ros}\) and ENF reference signals. For any ENF-containing video of an unknown source camera, our approach will analyze the video using different \(T_{ro}\) values. The estimated ENF signals are matched against the reference signals, and the performance metrics--normalized cross-correlation (NCC), root mean squared error (RMSE), and mean absolute error (MAE)--are then calculated with respect to the reference ENF and stored in a database. The \(T_{ro}\) (camera) with the highest NCC and lowest RMSE and MAE can then be identified. Our experimental analysis reveals that our proposed approach is very useful in identifying a source camera in the intended scenario. We summarize the contribution of this paper as follows:  
* We provide a review of ENF extraction from video recordings and the impact of the rolling shutter on ENF extraction. * We propose a novel ENF-based approach to identify the rolling shutter camera used to capture an ENF-containing video of unknown source. The remaining sections of this paper are structured as follows: Section 2 describes basic ENF concepts, reviews the ENF extraction method for video files, explains the impact of the rolling shutter, and discusses camera read-out time and ENF estimation. Section 3 presents the experiment conducted. Section 4 provides the results and discussion. Section 5 concludes the paper.