Search results
Results from the WOW.Com Content Network
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.
JPEG-HDR is a file format from Dolby Labs similar to RGBE encoding, standardized as JPEG XT Part 2. JPEG XT Part 7 includes support for encoding floating point HDR images in the base 8-bit JPEG file using enhancement layers encoded with four profiles (A-D); Profile A is based on the RGBE format and Profile B on the XDepth format from Trellis ...
PNG.png image/png Gecko 1.9 and Opera: Yes Apple Icon Image: Apple Inc..icns macOS: ART: AOL.art ASCII art.txt, .ansi, .text text/vnd.ascii-art Supported by GIMP: AutoCAD DXF: Drawing Interchange Format Autodesk.dxf image/vnd.dxf ARW: Sony Alpha RAW Sony: TIFF .arw AVIF: AV1 Image File Format Alliance for Open Media (AOMedia) AV1.avif image ...
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 ...
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.
File verification is the process of using an algorithm for verifying the integrity of a computer file, usually by checksum.This can be done by comparing two files bit-by-bit, but requires two copies of the same file, and may miss systematic corruptions which might occur to both files.
This attack is normally harder, a hash of n bits can be broken in 2 (n/2)+1 time steps, but is much more powerful than a classical collision attack. Mathematically stated, given two different prefixes p 1, p 2, the attack finds two suffixes s 1 and s 2 such that hash(p 1 ∥ s 1) = hash(p 2 ∥ s 2) (where ∥ is the concatenation operation).
Fuzzy hashing exists to solve this problem of detecting data that is similar, but not exactly the same, as other data. Fuzzy hashing algorithms specifically use algorithms in which two similar inputs will generate two similar hash values. This property is the exact opposite of the avalanche effect desired in cryptographic hash functions.