Image editing software is so ubiquitous and easy to use, according to researchers from QUT’s Digital Media Research Centre, it’s got the power to re-imagine history.
A peace sign from Martin Luther King, Jr, becomes a rude gesture; President Donald Trump’s inauguration crowd scenes inflated; dolphins in Venice’s Grand Canal; and crocodiles on the roads of bombarded Townsville – all manipulated images posted as reality.
And, they say, deadline-driven journalists lack the resources to tell, especially when the images come through from social networking.
Their study, Visual mis/disinformation in journalism and public communications, was published in Journalism Practice. It had been driven by the greater prevalence of bogus news and the way societal media platforms and information businesses are trying to identify and fight visual mis/disinformation presented to their audiences.
“When Donald Trump’s staff posted an image to his official Facebook page in 2019, journalists were able to spot the photoshopped edits to the president’s skin and physique because an unedited version exists on the White House’s official Flickr feed,” said lead author Dr T.J. Thomson.
“But what about when unedited versions aren’t available online and journalists can’t rely on simple reverse-image searches to verify whether an image is real or has been manipulated?
“When it is possible to alter past and present images, by methods like cloning, splicing, cropping, re-touching or re-sampling, we face the danger of a re-written history – a very Orwellian scenario.”
Examples highlighted from the report include images shared by information outlets last year of crocodiles on Townsville streets during flooding that were later demonstrated to be images of alligators in Florida in 2014. In addition, it quotes a Reuters worker on their discovery that a harrowing movie shared throughout Cyclone Idai, which ravaged parts of Africa in 2019, had been shot in Libya five years before.
This edited version was shared broadly on Twitter, Reddit, and white supremacist site The Daily Stormer.
Edward Hurcombe, and Adam Smith have mapped journalists’ current social media verification methods and suggest which tools are most effective for which circumstances.
“Detection of false images is made harder by the number of visuals created daily – in excess of 3.2 billion photos and 720,000 hours of video – along with the speed at which they are produced, published, and shared,” said Dr Thomson.
“Other considerations include the digital and visual literacy of those who see them. Yet being able to detect fraudulent edits masquerading as reality is critically important.”
“While journalists who create visual media are not immune to ethical breaches, the practice of incorporating more user-generated and crowd-sourced visual content into news reports is growing. Verification on social media will have to increase commensurately if we wish to improve trust in institutions and strengthen our democracy.”
Dr Thomson said a recent quantitative study performed from the International Centre for Journalists (ICFJ) found a very low usage of social networking verification tools in newsrooms.
“The ICFJ surveyed over 2,700 journalists and newsroom managers in more than 130 countries and found only 11% of those surveyed used social media verification tools,” he said.
“The lack of user-friendly forensic tools available and low levels Of electronic media literacy, combined, are chief hurdles to those trying to stem the tide of visual mis/disinformation online.”
Associate Professor Angus stated the study revealed an urgent need For better instruments, designed with journalists, to provide increased clarity across the provenance and authenticity of images and other media.
Visual content they encounter, journalists have to quickly decide whether to re-publish or enhance this material,” he explained.
The researchers state present manual detection approaches – using a reverse image search, analyzing image metadata, analyzing light and shadows; and using image editing applications – but say tools need to Be developed, such as more advanced machine learning methods, to verify visuals on social networking.
Related Journal Article: https://www.tandfonline.com/doi/full/10.1080/17512786.2020.1832139