@article{choi_wong_su_wu_2023, title={Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters}, volume={18}, ISSN={["1556-6021"]}, DOI={10.1109/TIFS.2023.3287132}, abstractNote={Electric network frequency (ENF) analysis is a promising forensic technique for authenticating multimedia recordings and detecting tampering. The validity of the ENF analysis heavily relies on the capability of extracting high-quality ENF signals from multimedia recordings. This paper analyzes and compares two representative methods for extracting ENF signals from visual signals acquired by cameras using the rolling-shutter mechanism. The first method proposed in prior work, direct concatenation, ignores the idle period of each frame. The second method proposed in this paper, periodic zeroing-out, inserts zeros to missing sample points instead of ignoring the idle period. Our theoretical analyses of using multirate signal processing reveal and experiments confirm that while the first method can extract ENF signals without knowing the exact value of camera read-out time, there exists some mild distortion to extracted ENF signals. In contrast, the second method taking the read-out time as the additional input is capable of extracting distortion-free ENF signals, and its frequency component of the highest strength is always located at the nominal frequency. Additionally, we examine aliased DC and negative ENF components caused by the two methods and show that their impact on the accuracy of frequency estimation is minimum. This paper facilitates the fundamental understanding of extracting ENF signals from videos. The research findings imply that the periodic zeroing-out method offers more accurate frequency estimates, but the performance improvement is moderate.}, journal={IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY}, author={Choi, Jisoo and Wong, Chau-Wai and Su, Hui and Wu, Min}, year={2023}, pages={4229–4242} }
 @article{choi_wong_hajj-alunado_wa_ren_2022, title={Invisible Geolocation Signature Extraction From a Single Image}, volume={17}, ISSN={["1556-6021"]}, DOI={10.1109/TIFS.2022.3185775}, abstractNote={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.}, journal={IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY}, author={Choi, Jisoo and Wong, Chau-Wai and Hajj-AlunadO, Adi and Wa, Min and Ren, Yanpin}, year={2022}, month={Jun}, pages={2598–2613} }