Electrical network frequency analysis: Difference between revisions

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'''Electrical network frequency''' ('''ENF''') '''analysis''' is aan [[forensicaudio scienceforensics]] technique for validating [[audio recording]]s by comparing frequency changes in background [[mains hum]] in the recording with long-term high-precision historical records of [[mains frequency]] changes from a database. In effect the mains hum signal is treated as if it waswere a time-dependent [[digital watermark]] that can be used tohelp identify the time at whichwhen the recording was created, and to help detect any edits in the soundrecording, or disprove tampering of a recording.<ref> Cooper, A.J: {{cite web|url=http://www.aes.org/e-lib/browse.cfm?elib=14411|title=The electric network frequency (ENF) as an aid to authenticating forensic digital audio recordings – an automated approach|date=June 2008}}, Conference paper, AES 33rd International Conference, USA (2008)</ref><ref> Grigoras, C. : {{cite web|url=http://www.equinoxjournals.com/IJSLL/article/viewArticle/525|title=Digital audio recording analysis – the electric network frequency criterion|access-date=2010-06-06|archive-date=2012-03-07|archive-url=https://web.archive.org/web/20120307075712/http://www.equinoxjournals.com/IJSLL/article/viewArticle/525|url-status=dead}}, International Journal of Speech Language and the Law, vol. 12, no. 1, pp. 6363–76 (2005)</ref><ref>Mateusz Kajstura, Agata Trawinska, Jacek Hebenstreit. {{cite web|url=http://www.fsijournal.org/article/S0379-760738%2804%2900766-2/abstract|title=Application of the Electrical Network Frequency (ENF) Criterion: A case of a digital recording}} Forensic Science International, Volume 155, Issue 2, Pages 165–171 (20 December 2005)</ref><ref name="williams_article">{{cite web |title=Met lab claims 'biggest breakthrough since Watergate' |url=https://www.theregister.com/2010/06/01/enf_met_police/ |first=Christopher |last=Williams |publisher=[[The Register]] |date=June 1, 2010 |access-date=September 15, 2021}}</ref> Historical records of mains frequency changes are kept on record, e.g., by police in the German federal state of [[Bavaria]] since 2010<ref>{{cite web|url=http://www.sueddeutsche.de/muenchen/landeskriminalamt-dem-verbrechen-auf-der-spur-1.1061222-2|title=Dem Verbrechen auf der Spur|language=german|publisher=Süddeutsche Zeitung|date=2011-02-16}}</ref> and the [[United Kingdom]] [[Metropolitan Police]] since 2005.<ref name="williams_article"/>

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The technology has been hailed as the '"the most significant development in [[audio forensics]] since [[Watergate scandal|Watergate]].'"<ref>{{cite web|urlname=http:"williams_article"//www.theregister.co.uk/2010/06/01/enf_met_police/|title=Met lab claims 'biggest breakthrough since Watergate'|author=Chris Williams|publisher=The Register|date=2010-06-01}}</ref>. However, according to a paper by Huijbregtse and Geradts, the ENF technique, although powerful, has significant limitations caused by confusionambiguity based on fixed frequency offsets during recording, and [[self-similarity]] within the mains frequency database, particularly for recordings shorter than 10 minutes.<ref>Maarten Huijbregtse, Zeno Geradts. {{cite web|url=http://www.forensic.to/ENF%20processed.pdf|title=Using the ENF criterion for determining the time of recording of short digital audio recordings}} Lecture Notes Inin Computer Science; Vol. 5718, Proceedings of the 3rd International Workshop on Computational Forensics, 2009.</ref>

'''Electrical network frequency''' ('''ENF''') '''analysis''' is a [[forensic science]] technique for validating [[audio recording]]s by comparing frequency changes in background [[mains hum]] in the recording with long-term high-precision historical records of [[mains frequency]] changes from a database. In effect the mains hum signal is treated as if it was a time-dependent [[digital watermark]] that can be used to identify the time at which the recording was created, and to help detect any edits in the sound recording.<ref> Cooper, A.J: {{cite web|url=http://www.aes.org/e-lib/browse.cfm?elib=14411|title=The electric network frequency (ENF) as an aid to authenticating forensic digital audio recordings – an automated approach}}, Conference paper, AES 33rd International Conference, USA (2008)</ref><ref> Grigoras, C. : {{cite web|url=http://www.equinoxjournals.com/IJSLL/article/viewArticle/525|title=Digital audio recording analysis – the electric network frequency criterion}}, International Journal of Speech Language and the Law, vol. 12, no. 1, pp. 63-76 (2005)</ref>

More recently, researchers demonstrated that indoor lights such as fluorescent lights and incandescent bulbs vary their light intensity in accordance with the voltage supplied, which in turn depends on the voltage supply frequency. As a result, the light intensity can carry the frequency fluctuation information to the visual sensor recordings in a similar way as the electromagnetic waves from the power transmission lines carry the ENF information to audio sensing mechanisms. Based on this result, researchers demonstrated that visual track from still video taken in indoor lighting environments also contain ENF traces that can be extracted by estimating the frequency at which ENF will appear in a video as low sampling frequency of video (25–30&nbsp;Hz) cause significant aliasing.<ref>Garg Ravi, Varna Avinash L., Wu Min: {{cite book|url=http://dl.acm.org/citation.cfm?id=2072303|title="Seeing" ENF: natural time stamp for digital video via optical sensing and signal processing|date=28 November 2011 |pages=23–32 |doi=10.1145/2072298.2072303 |isbn=9781450306164 |s2cid=8009218 }}, Conference paper, in proceedings of the 19th international ACM Multimedia Conference, USA (2011)</ref> It was also demonstrated in the same research that the ENF signatures from the visual stream and the ENF signature from the audio stream in a given video should match. As a result, the matching between the two signals can be used to determine if the audio and visual track were recorded together or superimposed later.<ref>{{cite web|last=Garg |first=Ravi |title=Research Projects |url=http://www.ece.umd.edu/~ravig/Research.html |archive-url=https://archive.today/20120805080953/http://www.ece.umd.edu/~ravig/Research.html |url-status=dead |archive-date=2012-08-05 |work=ece.umd.edu }}</ref>

The technology has been hailed as the 'the most significant development in audio forensics since [[Watergate scandal|Watergate]].'<ref>{{cite web|url=http://www.theregister.co.uk/2010/06/01/enf_met_police/|title=Met lab claims 'biggest breakthrough since Watergate'|author=Chris Williams|publisher=The Register|date=2010-06-01}}</ref>. However, according to a paper by Huijbregtse and Geradts, the ENF technique, although powerful, has significant limitations caused by confusion based on fixed frequency offsets during recording, and self-similarity within the mains frequency database, particularly for recordings shorter than 10 minutes.<ref>Maarten Huijbregtse, Zeno Geradts. {{cite web|url=http://www.forensic.to/ENF%20processed.pdf|title=Using the ENF criterion for determining the time of recording of short digital audio recordings}} Lecture Notes In Computer Science; Vol. 5718, Proceedings of the 3rd International Workshop on Computational Forensics, 2009.</ref>

== Use by law enforcement ==

The distinctive electrical hums have been used to provide forensic verification of audio recordings, a process fully automated in the [[United Kingdom]].<ref>{{cite news|last1=Morelle|first1=Rebecca|author-link=Rebecca Morelle|title=The hum that helps to fight crime|url=https://www.bbc.co.uk/news/science-environment-20629671|accessdate=27 January 2017|date=12 December 2012}}</ref>

== References ==

{{reflist}}

== External links ==

{{forensics-stub}}

*[https://www.youtube.com/watch?v=e0elNU0iOMY The hidden background noise that can catch criminals] (Tom Scott/YouTube)

[[Category:Forensics]]

[[Category:Electric power]]

[[Category:Sound recording]]

[[Category:ForensicsForensic techniques]]