Research reported in perceptualJanuary hashing2019 at [[Northumbria University]] has shown for video atit can be used to simultaneously identify similar contents for [[Northumbriavideo Universitycopy 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> has shown 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.
Similarly,Research researchreported in May 2020 by the [[University of Houston]] in deep learning based perceptual hashing for audio byhas theshown better performance than traditional [[Universityaudio of Houstonfingerprinting]] methods for the detection of similar/copied audio subject to transformations.<ref name=bs20>{{Cite journal |last=Báez-Suárez |first=Abraham |last2=Shah |first2=Nolan |last3=Nolazco-Flores |first3=Juan Arturo |last4=Huang |first4=Shou-Hsuan S. |last5=Gnawali |first5=Omprakash |last6=Shi |first6=Weidong |date=2020-05-19 |title=SAMAF: Sequence-to-sequence Autoencoder Model for Audio Fingerprinting |url=https://doi.org/10.1145/3380828 |journal=ACM Transactions on Multimedia Computing, Communications, and Applications |volume=16 |issue=2 |pages=43:1–43:23 |doi=10.1145/3380828 |issn=1551-6857}}</ref> has shown better performance than traditional [[audio fingerprinting]] methods for the detection of similar/copied audio subject to transformations.
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, athe group of researchers 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>
A In addition, aChinese team fromreported Chinain discoveredJuly 2019 that applyingthey had discovered a perceptual hashinghash tofor [[speech encryption]] which 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.<ref name=zhang19>{{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>
