Exploiting Power Signatures for Camera Forensics

Hajj-Ahmad, Adi; Berkovich, Andrew; Wu, Min

doi:10.1109/lsp.2016.2537201

Cited by 38 publications

(38 citation statements)

References 13 publications

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“…Camera forensics has become a significant field of research for various applications such as verification query image/video pairs can be attributed to the same source camera, and verification the source of a suspicious image/video [23], [24]. To the best of our knowledge, the only work exploiting ENF for camera forensics is that of Hajj-Ahmad et al [18]. They basically compute camera read-out time per frame based on vertical phase analysis [21].…”

Section: Idle Period Estimationmentioning

confidence: 99%

“…where L is number of rows) for each single row [18]. Φ [l] can be extracted through the Fourier Transform of the lthrow time-series that can be obtained by using the lth-row mean intensity of each frame of the video.…”

Section: A State-of-the-art Approachmentioning

confidence: 99%

“…Unlike to the state-of-the-art [18] which treats the sequence of mean luminance values of the i th rows of a sequence of frames of the video as a separate time series, the proposed approach concatenates the illumination samples of all rows of all consecutive frames. Hence, the sampling rate for the proposed approach is as high as video frame rate × number of rows as discussed in section II, which is much higher than the Nyquist Criteria, which leads the proposed approach to operate on videos with any frame rate, i.e., no alias ENF.…”

Section: B Proposed Approachmentioning

confidence: 99%

“…Estimated idle period is obtained based on the search of the corresponding match between the reference parameters and the test video parameters. The expected idle period for the 30 fps videos was obtained from still-content videos in section III-A by using vertical phase analysis [18]. The expected idle period for the 25 fps video set is obtained in section III-C mainly based on the proposed approach.…”

Section: Experiments With Videos With Moving Contentmentioning

confidence: 99%

“…The estimated idle period is in the range between 20% and 25%. Since the video frame rate is half the nominal ENF, i.e., 0 Hz alias ENF, idle period cannot be computed via [18] for this case. value of estimated idle periods for the videos by each camera and average estimation error based on the estimated median.…”

Section: Experiments With Videos With Moving Contentmentioning

confidence: 99%

See 4 more Smart Citations

Analysis of Rolling Shutter Effect on ENF-Based Video Forensics

Vatansever

Dirik

Memon

2019

IEEE Trans.Inform.Forensic Secur.

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ENF is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. These fluctuations in luminance can be captured by video recordings. Accordingly, ENF can be estimated from such videos by analysis of steady content in the video scene. When videos are captured by using a rolling shutter sampling mechanism, as is done mostly with CMOS cameras, there is an idle period between successive frames. Consequently, a number of illumination samples of the scene are effectively lost due to the idle period. These missing samples affect ENF estimation, in the sense of the frequency shift caused and the power attenuation that results. This work develops an analytical model for videos captured using a rolling shutter mechanism. The model illustrates how the frequency of the main ENF harmonic varies depending on the idle period length, and how the power of the captured ENF attenuates as idle period increases. Based on this, a novel idle period estimation method for potential use in camera forensics that is able to operate independently of video frame rate is proposed. Finally, a novel time-of-recording verification approach based on use of multiple ENF components, idle period assumptions and interpolation of missing ENF samples is also proposed.

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Section: Idle Period Estimationmentioning

confidence: 99%

Section: A State-of-the-art Approachmentioning

confidence: 99%

Section: B Proposed Approachmentioning

confidence: 99%

Section: Experiments With Videos With Moving Contentmentioning

confidence: 99%

Section: Experiments With Videos With Moving Contentmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Rolling Shutter Effect on ENF-Based Video Forensics

Vatansever

Dirik

Memon

2019

IEEE Trans.Inform.Forensic Secur.

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Modelling GOP Structure Effects on ENF-Based Video Forensics

Ferrara

Draper-Gil

Sánchez

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Power Signature for Multimedia Forensics

Hajj-Ahmad

Wong

Choi

et al. 2022

Multimedia Forensics

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There has been an increasing amount of work surrounding the Electric Network Frequency (ENF) signal, an environmental signature captured by audio and video recordings made in locations where there is electrical activity. ENF is the frequency of power distribution networks, 60 Hz in most of the Americas and 50 Hz in most other parts of the world. The ubiquity of this power signature and the appearance of its traces in media recordings motivated its early application toward time–location authentication of audio recordings. Since then, more work has been done toward utilizing this signature for other forensic applications, such as inferring the grid in which a recording was made, as well as applications beyond forensics, such as temporally synchronizing media pieces. The goal of this chapter is to provide an overview of the research work that has been done on the ENF signal and to provide an outlook for the future.

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Exploiting Power Signatures for Camera Forensics

Cited by 38 publications

References 13 publications

Analysis of Rolling Shutter Effect on ENF-Based Video Forensics

Analysis of Rolling Shutter Effect on ENF-Based Video Forensics

Modelling GOP Structure Effects on ENF-Based Video Forensics

Power Signature for Multimedia Forensics

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