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==Development==
The 1980 work of [[Marr–Hildreth algorithm|Marr and Hildreth]] is a seminal paper in this field.<ref name=marr80>{{Cite journal |title=Theory of Edge Detection |
The July 2010 thesis of Zauner is a well-written introduction to the topic.<ref name="zauner10">{{cite book |last1=Zauner |first1=Christoph |title=Implementation and Benchmarking of Perceptual Image Hash Functions |date= July 2010 |publisher=Upper Austria University of Applied Sciences, Hagenberg Campus |url=https://www.phash.org/docs/pubs/thesis_zauner.pdf}}</ref>
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Researchers remarked in December 2017 that [[Google image search]] is based on a perceptual hash.<ref name="agis">{{cite news |title=Google Image Search Explained |url=https://alibaba-cloud.medium.com/google-image-search-explained-30af8ba9cbea |publisher=Medium |date=26 December 2017}}</ref>
In research published in November 2021 investigators focused on a manipulated image of [[Stacey Abrams]] which was published to the internet prior to her loss in the [[2018 Georgia gubernatorial election]]. They found that the pHash algorithm was vulnerable to nefarious actors.<ref name="hao21">{{cite journal |last1=Hao |first1=Qingying |last2=Luo |first2=Licheng |last3=Jan |first3=Steve T.K. |last4=Wang |first4=Gang |title=
==Characteristics==
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Research reported in January 2019 at [[Northumbria University]] has shown for video it can be used to simultaneously identify similar contents for [[video copy detection]] and detect malicious manipulations for video authentication. The system proposed performs better than current [[video hash]]ing techniques in terms of both identification and authentication. <ref name=khelifi19>{{cite journal |last1=Khelifi |first1=Fouad |last2=Bouridane |first2=Ahmed |title=Perceptual Video Hashing for Content Identification and Authentication |journal=IEEE Transactions on Circuits and Systems for Video Technology |date=January 2019 |volume=29 |issue=1 |pages=50–67 |doi=10.1109/TCSVT.2017.2776159 |s2cid=55725934 |url=http://nrl.northumbria.ac.uk/32873/1/paper_double.pdf }}</ref>
Research reported in May 2020 by the [[University of Houston]] in deep learning based perceptual hashing for audio has shown better performance than traditional [[audio fingerprinting]] methods for the detection of similar/copied audio subject to transformations.<ref name=bs20>{{Cite journal |
In addition to its uses in digital forensics, research by a Russian group reported in 2019 has shown that perceptual hashing can be applied to a wide variety of situations. Similar to comparing images for copyright infringement, the group found that it could be used to compare and match images in a database. Their proposed algorithm proved to be not only effective, but more efficient than the standard means of database image searching.<ref name=zak19>{{cite book |last1=Zakharov |first1=Victor |last2=Kirikova |first2=Anastasia |last3=Munerman |first3=Victor |last4=Samoilova |first4=Tatyana |title=2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EICon ''Rus'') |chapter=Architecture of Software-Hardware Complex for Searching Images in Database |pages=1735–1739 |publisher=IEEE |doi=10.1109/EIConRus.2019.8657241 |isbn=978-1-7281-0339-6 |year=2019 |s2cid=71152337 }}</ref>
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