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{{Short description|Class of fingerprinting algorithm}}
'''Perceptual hashing''' is the use of a [[fingerprinting algorithm]] that produces a snippet or [[Fingerprint (computing)|fingerprint]] of various forms of [[multimedia]].<ref name=buldas13>{{cite book|last1=Buldas|first1=Ahto|last2=Kroonmaa|first2=Andres|last3=Laanoja|first3=Risto|editor-last=Riis|editor-first=Nielson H.|editor-last2=Gollmann|editor-first2=D.|title=Secure IT Systems. NordSec 2013|chapter=Keyless Signatures’ Infrastructure: How to Build Global Distributed Hash-Trees|publisher=Springer|___location=Berlin, Heidelberg|year=2013|isbn=978-3-642-41487-9|issn=0302-9743|doi=10.1007/978-3-642-41488-6_21|series=Lecture Notes in Computer Science|volume=8208|quote=Keyless Signatures Infrastructure (KSI) is a globally distributed system for providing time-stamping and server-supported digital signature services. Global per-second hash trees are created and their root hash values published. We discuss some service quality issues that arise in practical implementation of the service and present solutions for avoiding single points of failure and guaranteeing a service with reasonable and stable delay. Guardtime AS has been operating a KSI Infrastructure for 5 years. We summarize how the KSI Infrastructure is built, and the lessons learned during the operational period of the service.}}</ref><ref name=klinger>{{cite web | last1=Klinger | first1=Evan | last2=Starkweather | first2=David |title=pHash.org: Home of pHash, the open source perceptual hash library | website=pHash.org | url=http://www.phash.org/ | ref={{sfnref | pHash.org}} | access-date=2018-07-05|quote=pHash is an open source software library released under the GPLv3 license that implements several perceptual hashing algorithms, and provides a C-like API to use those functions in your own programs. pHash itself is written in C++.}}</ref> A perceptual [[hash function|hash]] is a type of [[locality-sensitive hash]], which is analogous if [[feature vector|features]] of the multimedia are similar. This is not to be confused with [[Cryptographic hash function|cryptographic hashing]], which relies on the [[avalanche effect]] of a small change in input value creating a drastic change in output value. Perceptual hash functions are widely used in finding cases of online [[copyright infringement]] as well as in [[digital forensics]] because of the ability to have a correlation between hashes so similar data can be found (for instance with a differing [[Digital watermark|watermark]]).
==Development==
The 1980 work of [[Marr and Hildreth]] is a seminal paper in this field.<ref name=marr80>{{Cite journal |title=Theory of Edge Detection |first=D. |last=Marr |author1-link=David Marr (neuroscientist) |first2=E. |last2=Hildreth |author2-link=Ellen Hildreth |journal=Proceedings of the Royal Society of London. Series B, Biological Sciences |volume=207 |number=1167 |date=29 Feb 1980 |pages=187–217 |doi=10.1098/rspb.1980.0020|pmid=6102765 }}</ref>
The 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=University of Hagenburg |url=https://www.phash.org/docs/pubs/thesis_zauner.pdf}}</ref>
Already in 2016, Asgari published work on robust image hash spoofing. Asgari notes that perceptual hash function like any other algorithm is prone to errors.<ref name="asgari16">{{cite book |last1=Asgari |first1=Azadeh Amir |title=Robust image hash spoofing |date=June 2016 |publisher=Blekinge Institute of Technology |url=http://www.diva-portal.se/smash/get/diva2:946365/FULLTEXT01.pdf}}</ref>
==Characteristics==
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