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In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.
John Smith and Sandra Dee share the same hash value of 02, causing a hash collision. In computer science, a hash collision or hash clash [1] is when two distinct pieces of data in a hash table share the same hash value. The hash value in this case is derived from a hash function which takes a data input and returns a fixed length of bits. [2]
Perceptual hashing is the use of a fingerprinting algorithm that produces a snippet, hash, 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.
hash HAS-160: 160 bits hash HAVAL: 128 to 256 bits hash JH: 224 to 512 bits hash LSH [19] 256 to 512 bits wide-pipe Merkle–Damgård construction: MD2: 128 bits hash MD4: 128 bits hash MD5: 128 bits Merkle–Damgård construction: MD6: up to 512 bits Merkle tree NLFSR (it is also a keyed hash function) RadioGatún: arbitrary ideal mangling ...
The following tables compare general and technical information for a number of cryptographic hash functions. See the individual functions' articles for further information. This article is not all-inclusive or necessarily up-to-date. An overview of hash function security/cryptanalysis can be found at hash function security summary.
What is needed is a hash function H(z,n) (where z is the key being hashed and n is the number of allowed hash values) such that H(z,n + 1) = H(z,n) with probability close to n/(n + 1). Linear hashing and spiral hashing are examples of dynamic hash functions that execute in constant time but relax the property of uniformity to achieve the ...
Video fingerprinting or video hashing are a class of dimension reduction techniques [1] in which a system identifies, extracts and then summarizes characteristic components of a video as a unique or a set of multiple perceptual hashes or fingerprints, enabling that video to be uniquely identified. This technology has proven to be effective at ...
However, this is achieved by only hashing a fraction of the file. This weakness makes it trivial to create a hash collision, allowing large sections to be completely altered without altering the checksum. This method is used by Kazaa. The weakness of UUHash is exploited by anti-p2p agencies to corrupt downloads. [1]