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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.
Sometimes, you find a drawing or similar image useful for a Wikipedia article, that was saved as a JPEG but should have been saved as a PNG.JPEG is good for images where the color changes fluidly throughout the image, like in a photograph, whereas PNG files are good for images with relatively few colors, such as a drawing of a flag, a chart, or a map; note that sometimes SVG is better.
The Secure Hash Algorithms are a family of cryptographic hash functions published by the National Institute of Standards and Technology (NIST) as a U.S. Federal Information Processing Standard (FIPS), including: SHA-0: A retronym applied to the original version of the 160-bit hash function published in 1993 under the name "SHA". It was ...
The salt and hash are then stored in the database. To later test if a password a user enters is correct, the same process can be performed on it (appending that user's salt to the password and calculating the resultant hash): if the result does not match the stored hash, it could not have been the correct password that was entered.
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 ...
Such file hashes are often the top hash of a hash list or a hash tree, which allows for additional benefits. One of the main applications of a hash function is to allow the fast look-up of data in a hash table. Being hash functions of a particular kind, cryptographic hash functions lend themselves well to this application too.
BLAKE is a cryptographic hash function based on Daniel J. Bernstein's ChaCha stream cipher, but a permuted copy of the input block, XORed with round constants, is added before each ChaCha round. Like SHA-2 , there are two variants differing in the word size.
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.