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Fake news: a technological approach to proving the origins of content, using blockchains

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journal contribution
posted on 2023-06-09, 08:44 authored by Steve Huckle, Martin WhiteMartin White
In this paper, we introduce a prototype of an innovative technology for proving the origins of captured digital media. In an era of fake news, when someone shows us a video or picture of some event, how can we trust its authenticity? It seems the public no longer believe that traditional media is a reliable reference of fact, perhaps due, in part, to the onset of many diverse sources of conflicting information, via social media. Indeed, the issue of ‘fake’ reached a crescendo during the 2016 US Presidential Election, when the winner, Donald Trump, claimed that the New York Times was trying to discredit him by pushing disinformation. Current research into overcoming the problem of fake news does not focus on establishing the ownership of media resources used in such stories - the blockchain-based application introduced in this article is technology that is capable of indicating the authenticity of digital media. Put simply; by using the trust mechanisms of blockchain technology, the tool can show, beyond doubt, the provenance of any source of digital media, including images used out of context in attempts to mislead. Although the application is an early prototype and its capability to find fake resources is Peer Review Only/Not for Distributionsomewhat limited, we outline future improvements that would overcome such limitations. Furthermore, we believe our application (and its use of blockchain technology and standardised metadata), introduces a novel approach to overcoming falsities in news reporting and the provenance of media resources used therein. However, while our application has the potential to be able to verify the originality of media resources, we believe technology is only capable of providing a partial solution to fake news. That is because it is incapable of proving the authenticity of a news story as a whole. We believe that takes human skills.


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Big Data




Mary Ann Liebert





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