Abstract
Determining the location of a video is crucial for law enforcement and national security. While outdoor videos can use landmarks or geolocation techniques, indoor videos present a greater challenge due to the lack of visible landmarks. Recently, researchers have explored using the electrical network frequency (ENF), naturally embedded in audio and video sources, for this purpose. Most studies have focused on extracting ENF from different electrical grids (inter-grid scenarios), where ENF from one grid differs significantly from another. However, recent research shows that ENF recordings from different locations within the same grid (intra-grid scenarios) have minor variations that can help estimate the video's location. Previous work used ENF recordings directly from electrical mains. This paper proposes a technique to extract ENF from smartphone videos in intra-grid scenarios using an improved super-pixel approach. Unlike direct ENF extraction from electrical mains, extracting ENF from smartphone videos is challenging due to sensor noise and aliasing effects. Our improved super-pixel technique mitigates these issues. We validated our approach with real-world smartphone video recordings from various locations within the Australian Eastern grid. By comparing the extracted ENF signatures with ground truth ENF data from anchor locations provided by a power supplier, we demonstrated the ability to estimate the location of smartphone videos within the same electrical grid. To our knowledge, this is the first practical demonstration of location estimation for smartphone videos in an intra-grid scenario.