TinEye

Reverse image search engine

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TinEye

Type of site

| Image Search Engine
Available in| multilingual
Owner| Idée, Inc.
URL| tineye.com
Commercial| Yes
Registration| Optional
Launched| May 6, 2008; 16 years ago (2008-05-06)
Current status| Active

TinEye is a reverse image search engine developed and offered by Idée, Inc., a company based in Toronto, Ontario, Canada. It is the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks.[1][non-primary source needed] TinEye allows users to search not using keywords but with images. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of the image and matches it with other indexed images.[1] This procedure is able to match even heavily edited versions of the submitted image, but will not usually return similar images in the results.[1]

It has been recommended for fact-checking images by various fact-checkers.[citation needed]

## History

[edit]

Idée, Inc. was founded by Leila Boujnane and Paul Bloore in 1999. Idée launched the service on May 6, 2008 and went into open beta in August that year.[2][3] While computer vision and image identification research projects began as early as the 1980s,[4] the company claims that TinEye is the first web-based image search engine to use image identification technology. The service was created with copyright owners and brand marketers as the intended user base, to look up unauthorized use and track where the brands are showing up respectively.[5]

In June 2014, TinEye claimed to have indexed more than five billion images for comparisons.[6] However, this is a relatively small proportion of the total number of images available on the World Wide Web.[7]

As of March 2024, TinEye's search results claim to have over 70 billion images indexed for comparison.[8]

## Technology

[edit]

A user uploads an image to the search engine (the upload size is limited to 20 MB) or provides a URL for an image or for a page containing the image. The search engine will look up other usage of the image in the internet, including modified images based upon that image, and report the date and time at which they were posted. TinEye does not recognize outlines of objects or perform facial recognition, but recognizes the entire image, and some altered versions of that image. This includes smaller, larger, and cropped versions of the image. TinEye has shown itself capable of retrieving different images from its database of the same subject, such as famous landmarks.[9]

TinEye is capable of searching for images in JPEG, PNG, WebP, GIF, BMP and TIFF format.[10]

Results generated from TinEye include the total number of matches in their database, a preview image, and the URL to each match. TinEye can sort results by best match, most changed, biggest image, newest, and oldest.

User registration is optional and offers storage of the user's previous queries. Other features include embeddable widgets and bookmarklets. TinEye has also released their commercial API.

## Usage

[edit]

TinEye's ability to search the web for specific images (and modifications of those images) makes it a potential tool for the copyright holders of visual works to locate infringements on their copyright. It also creates a possible avenue for people who are looking to make use of imagery under orphan works to find the copyright holders of that imagery. Being that orphan works can be defined as "copyrighted works whose owners are difficult or impossible to identify and/or locate,"[11] the use of TinEye could potentially remove the orphan work status from online images that can be found in its database.

### Fact-checking

[edit]

It has been recommended by fact-checkers as a useful resource in attempts to verify the origin of images.[12][13][14][15][16][17][18][19] As of 2019, TinEye specialized in copyright violations and finding exact versions of images online.[20]

## See also

[edit]

* Content-based image retrieval

## References

[edit]

1. ^ a b c "TinEye Reverse Image Search". tineye.com. Retrieved November 1, 2022.
2. ^ "Releases". Tineye.com. Archived from the original on July 17, 2011. Retrieved February 21, 2013.
3. ^ Claburn, Thomas (August 18, 2008). "TinEye Image Search Finds Copyright Infringers". InformationWeek. Retrieved September 28, 2014.
4. ^ Szeliski, Richard (2010). Computer Vision: Algorithms and Applications. Springer Publishing. p. 832\. ISBN 978-1-84882-934-3.
5. ^ George-Cosh, David (n.d.). "Idée's TinEye next frontier in Web searches" (PDF). National Post. Retrieved February 11, 2010.
6. ^ "Retrieved 2014-07-01". Tineye.com. Archived from the original on July 1, 2014. Retrieved July 1, 2014.
7. ^ "Flickr hosts 5bn images as at Sep 10 – Retrieved 2011-04-06". Royal.pingdom.com. Archived from the original on July 12, 2018. Retrieved February 21, 2013.
8. ^ "TinEye Reverse Image Search". tineye.com. Retrieved August 23, 2023.
9. ^ Elias, Jean-Claude. (December 11, 2009). Search by photo. The Jordan Times. Retrieved on 2/19/10 from Factiva database.
10. ^ "TinEye Developer Documentation". services.tineye.com. Retrieved June 5, 2022.
11. ^ Yeh, B. (February 1, 2010). "Orphan works" in copyright law. Congressional Research Service. Retrieved on 2/19/10 from Factiva database.
12. ^ Ruggeri, Amanda (May 10, 2024). "The 'Sift' strategy: A four-step method for spotting misinformation". BBC. Retrieved July 16, 2024.
13. ^ Settles, Gabrielle (April 19, 2023). "PolitiFact - How to detect deepfake videos like a fact-checker". PolitiFact.
14. ^ "Election Misinformation Symposium" (PDF). Center for Media Engagement. Archived from the original (PDF) on December 9, 2022. Retrieved January 7, 2024.
15. ^ Holan, Angie Drobnic (March 31, 2022). "PolitiFact - PolitiFact's checklist for thorough fact-checking". PolitiFact. Archived from the original on July 1, 2022. Retrieved January 7, 2024.
16. ^ Angus, Daniel; Dootson, Paula; Thomson, T. J. (February 26, 2022). "Fake viral footage is spreading alongside the real horror in Ukraine. Here are 5 ways to spot it". The Conversation. Archived from the original on June 29, 2023. Retrieved January 7, 2024.
17. ^ Evon, Dan (March 22, 2022). "Snopes Tips: A Guide To Performing Reverse Image Searches". Snopes. Archived from the original on February 7, 2023. Retrieved January 7, 2024.
18. ^ Wilks-Harper, Ella (July 5, 2018). "7 verification tools for better fact-checking". Reuters News Agency. Archived from the original on September 25, 2022. Retrieved January 7, 2024.
19. ^ LaCapria, Kim (January 21, 2016). "6 Quick Ways to Spot Fake News". Snopes. Archived from the original on June 1, 2023. Retrieved January 7, 2024.
20. ^ Toler, Aric (December 26, 2019). "Guide To Using Reverse Image Search For Investigations". bellingcat. Retrieved July 16, 2024.

## External links

[edit]

* Official website