Invisible Geolocation Signature Extraction From a Single Image

• Published in: IEEE transactions on information forensics and security Vol. 17; pp. 2598 - 2613
• Main Authors: Choi, Jisoo, Wong, Chau-Wai, Hajj-Ahmad, Adi, Wu, Min, Ren, Yanpin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Audio data; Cameras; Dilution; electric network frequency (ENF); Electric power grids; Electrical networks; Electricity distribution; Embedding; Entropy; entropy minimization; Frequency estimation; Image classification; Location awareness; Optimization; Recording; Region-of-capturing localization; rolling shutter; Sensors; Time-frequency analysis
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2022.3185775

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 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.