Locality-sensitive hashing: Difference between revisions

Revision as of 16:56, 5 June 2023

Revision as of 14:40, 10 July 2023

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# The encoding must be robust against intentional attacks.

# The encoding should support an extremely low risk of false positives.

Testing performed in the paper on a range of file types identified the Nilsimsa hash as having a significantly higher false positive rate when compared to other similarity digest schemes such as TLSH, Ssdeep and Sdhash.

====TLSH====

'''TLSH''' is locality-sensitive hashing algorithm designed for a range of security and digital forensic applications.<ref name="TLSH"/> The goal of TLSH is to generate hash digests for messages such that low distances between digests indicate that their corresponding messages are likely to be similar.

Testing performed in the paper on a range of file types identified the Nilsimsa hash as having a significantly higher false positive rate when compared to other similarity digest schemes such as TLSH, Ssdeep and Sdhash.

An implementation of TLSH is available as [[open-source software]].<ref>{{cite web|url=https://github.com/trendmicro/tlsh |title=TLSH |website=[[GitHub]] |access-date=2014-04-10}}</ref>