{{Short description|class of fingerprinting algorithm}}
'''Perceptual hashing''' is the use of an [[fingerpintingfingerprinting algorithm]] that produces a snippet or [[Fingerprint (computing)|fingerprint]] of various forms of [[multimedia]].<ref>{{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>{{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]]). Based on research at [[Northumbria University]],<ref>{{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> it can also be applied 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.
In addition to its uses in digital forensics, research has shown that perceptual hashing can be applied to a wide variety of situations. Similar to comparing images for copyright infringement, a group of researchers<ref>{{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> 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. In addition, a team from China<ref>{{cite journal |last1=Zhang |first1=Qiu-yu |last2=Zhou |first2=Liang |last3=Zhang |first3=Tao |last4=Zhang |first4=Deng-hai |title=A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing |journal=Multimedia Tools and Applications |date=July 2019 |volume=78 |issue=13 |pages=17825–17846 |doi=10.1007/s11042-019-7180-9 |s2cid=58010160 }}</ref> discovered that applying perceptual hashing to speech encryption proved to be effective. They were able to create a system in which the encryption was not only more accurate, but more compact as well.
