Perceptual hashing

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Perceptual hashing is the use of an algorithm that produces a snippet or fingerprint of various forms of multimedia.[1][2] Perceptual hash functions are analogous if features are similar, whereas cryptographic hashing 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). For example, Wikipedia could maintain a database of text hashes of popular online books or articles for which the authors hold copyrights to, anytime a Wikipedia user uploads an online book or article that has a copyright, the hashes will be almost exactly the same and could be flagged as plagiarism. This same flagging system can be used for any multimedia or text file. 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 also more compact as well.


References[edit]

  1. ^ Buldas, Ahto; Kroonmaa, Andres; Laanoja, Risto (2013). "Keyless Signatures' Infrastructure: How to Build Global Distributed Hash-Trees". Secure IT Systems. Berlin, Heidelberg: Springer Berlin Heidelberg. 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". IEEE Transactions on Circuits and Systems for Video Technology. 29 (1): 50–67. doi:10.1109/TCSVT.2017.2776159.
  4. ^ Zakharov, Victor; Kirikova, Anastasia; Munerman, Victor; Samoilova, Tatyana. "Architecture of Software-Hardware Complex for Searching Images in Database". IEEE. Retrieved 1 August 2019.
  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.

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