Fake images: how much do they matter? Towards building quantitative modeling of misinformative images

There is little information in the literature about the specific impacts of image manipulation and misleading images in society. While some anecdotal references to qualitative factors exist, such factors are thinly covered in the literature, and estimates of quantitative, especially monetary, costs are even less available. That these costs are substantial is indicated by the 2019 study issued by cybersecurity firm CHEQ claiming fake news generally costs the global economy $78 billion annually, but the basis for such a figure has only been vaguely motivated. This paper explores potential impact factors relating to fake images that may provide a foundation for clarifying the quantitative impacts of misinformative online images in society. Models of quantitative costs are imperative to motivate governments and organisations to implement investment in new trust technologies as preventative measures, and give such organisations and institutions confidence that such expenditure is warranted and can deliver a beneficial return on investment. To overcome the sparsity of modelled information in this nascent field, a ‘scattergun’ approach was undertaken, in which computer science students were instructed to search online for examples of qualitative and quantitative costs of cybersecurity scam activities and fake images. 127 students responded, with a total of 341 examples, which were then grouped into broad categories of costs and separated into resultant and preventative costs. This paper further considers how such disparate information can inform quantitative modelling of costs to the global society and to individual organisations. Finally, the paper considers how adopting an authenticity framework such as the JPEG Trust standard might ameliorate such costs.