Perceptual hashing

Perceptual hashing is the use of an fingerprinting algorithm that produces a snippet or fingerprint of various forms of multimedia.^[1]^[2] A perceptual hash is a type of locality-sensitive hash, which is analogous if features of the multimedia are similar. This is not to be confused with 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 watermark). Based on research at Northumbria University,^[3] 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 hashing 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^[4] 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^[5] 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.

References[]

^ Buldas, Ahto; Kroonmaa, Andres; Laanoja, Risto (2013). "Keyless Signatures' Infrastructure: How to Build Global Distributed Hash-Trees". In Riis, Nielson H.; Gollmann, D. (eds.). Secure IT Systems. NordSec 2013. Lecture Notes in Computer Science. Vol. 8208. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-41488-6_21. ISBN 978-3-642-41487-9. ISSN 0302-9743. 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.
^ Klinger, Evan; Starkweather, David. "pHash.org: Home of pHash, the open source perceptual hash library". pHash.org. Retrieved 2018-07-05. 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++.
^ Khelifi, Fouad; Bouridane, Ahmed (January 2019). "Perceptual Video Hashing for Content Identification and Authentication" (PDF). IEEE Transactions on Circuits and Systems for Video Technology. 29 (1): 50–67. doi:10.1109/TCSVT.2017.2776159. S2CID 55725934.
^ Zakharov, Victor; Kirikova, Anastasia; Munerman, Victor; Samoilova, Tatyana (2019). "Architecture of Software-Hardware Complex for Searching Images in Database". 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EICon Rus). IEEE. pp. 1735–1739. doi:10.1109/EIConRus.2019.8657241. ISBN 978-1-7281-0339-6. S2CID 71152337.
^ Zhang, Qiu-yu; Zhou, Liang; Zhang, Tao; Zhang, Deng-hai (July 2019). "A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing". Multimedia Tools and Applications. 78 (13): 17825–17846. doi:10.1007/s11042-019-7180-9. S2CID 58010160.

External links[]

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[1] Buldas, Ahto; Kroonmaa, Andres; Laanoja, Risto (2013). "Keyless Signatures' Infrastructure: How to Build Global Distributed Hash-Trees". In Riis, Nielson H.; Gollmann, D. (eds.). Secure IT Systems. NordSec 2013. Lecture Notes in Computer Science. Vol. 8208. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-41488-6_21. ISBN 978-3-642-41487-9. ISSN 0302-9743. 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.

[2] Klinger, Evan; Starkweather, David. "pHash.org: Home of pHash, the open source perceptual hash library". pHash.org. Retrieved 2018-07-05. 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++.

[3] Khelifi, Fouad; Bouridane, Ahmed (January 2019). "Perceptual Video Hashing for Content Identification and Authentication" (PDF). IEEE Transactions on Circuits and Systems for Video Technology. 29 (1): 50–67. doi:10.1109/TCSVT.2017.2776159. S2CID 55725934.

[4] Zakharov, Victor; Kirikova, Anastasia; Munerman, Victor; Samoilova, Tatyana (2019). "Architecture of Software-Hardware Complex for Searching Images in Database". 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EICon Rus). IEEE. pp. 1735–1739. doi:10.1109/EIConRus.2019.8657241. ISBN 978-1-7281-0339-6. S2CID 71152337.

[5] Zhang, Qiu-yu; Zhou, Liang; Zhang, Tao; Zhang, Deng-hai (July 2019). "A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing". Multimedia Tools and Applications. 78 (13): 17825–17846. doi:10.1007/s11042-019-7180-9. S2CID 58010160.

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Perceptual hashing

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