Fuzzy hashing, also known as similarity hashing,[1] is a technique for detecting data that is similar, but not exactly the same, as other data. This is in contrast to cryptographic hash functions, which are designed to have significantly different hashes for even minor differences. Fuzzy hashing has been used to identify malware[2][3] and has potential for other applications, like data loss prevention and detecting multiple versions of code.[4][5]
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Fuzzyhashing, also known as similarity hashing, is a technique for detecting data that is similar, but not exactly the same, as other data. This is in...
character strings, but other suitable hash functions are also used. Fuzzyhashing, also known as similarity hashing, is a technique for detecting data that...
matching was taken in consideration by Jesse Kornblum when developing the fuzzyhashing in 2006, that used the algorithms of spamsum by Andrew Tridgell (2002)...
holds a patent on fault injection methods for software testing, and fuzzyhashing for computer forensics. Due to an email leak in 2011, Hoglund is well...
Widely used algorithms are based on filter-verification, hashing, Locality-sensitive hashing (LSH), Tries and other greedy and approximation algorithms...
submitted fuzzy checksum exceeds a certain threshold, the database notes that this probably indicates spam. ISP service users similarly generate a fuzzy checksum...
Fuzzy extractors are a method that allows biometric data to be used as inputs to standard cryptographic techniques, to enhance computer security. "Fuzzy"...
transformation like hashing, or even salting, the password); meanwhile, Eve is eavesdropping on the conversation and keeps the password (or the hash). After the...
King-Hong; Zhang, David; Kamel, Mohamed; You, Jane (2006). "An analysis of Bio Hashing and its variants". Pattern Recognition. 39 (7): 1359–1368. Bibcode:2006PatRe...
neighbour or k-nearest neighbors methods. Deep learning is useful in semantic hashing where a deep graphical model the word-count vectors obtained from a large...
Hamming distance Hunt–Szymanski algorithm Jaccard index Locality-sensitive hashing Longest common subsequence problem Lucene (an open source search engine...
Fowler–Noll–Vo hash function: fast with low collision rate Pearson hashing: computes 8 bit value only, optimized for 8 bit computers Zobrist hashing: used in...
include: Hierarchical Navigable Small World (HNSW) graphs Locality-sensitive Hashing (LSH) and Sketching Product Quantization (PQ) Inverted Files and combinations...
is also possible to use a cryptographic hash function as a randomness extractor. However, not every hashing algorithm is suitable for this purpose.[citation...
identical[citation needed] to the Feature hashing algorithm by John Moody, but differs in its use of hash functions with low dependence, which makes...
used for natural language processing. Computational creativity Semantic hashing Semantic Pointer Architecture Sparse distributed memory Amosov, N. M.,...
(history) Combinatorial game theory (pedagogy) Star (game theory) Zero game, fuzzy game Dots and boxes Impartial game Digital sum Nim Nimber Sprague–Grundy...
{\displaystyle p(c_{i}|x_{j})\,} has some overlap with the verbal fuzzy membership concept of fuzzy logic. An interesting extension is the case of information...
for extracting accurate geometry (ex: via cube marching), the process is fuzzy, as with most neural methods. This limits NeRF to cases where the output...
more than two truth values with the help of many-valued logic, such as fuzzy logic or Łukasiewicz logic. Ambiguous situations may cause humans to affirm...
is given a unique numerical number known as a hash. Google then scans Gmail looking for the unique hashes. When suspicious images are located Google reports...
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