Manipulated media assets such as images can be particularly compelling elements of scams and other online fraud. Even unmanipulated images can contribute to misinformation if presented out of context or used to support false narratives. With the emergence of AI generated content (AIGC), this problem has been raised to a new level. AIGC media flooding the online digital environment has mobilized governments to act: in the last few years China, the USA, the UK and the EU have all developed legislation mandating that AIGC media assets be clearly identified as such. [1] Simultaneously, international efforts in developing solutions are underway; in January of this year a new standard in media trustworthiness, ISO/IEC 21617-1:2025 (JPEG Trust – Core Foundation) was published by ISO/IEC JTC 1/SC 29/WG 1 (JPEG). In previous research by the authors, it was identified that the global financial price tag of dis-informative and/or miscontextualized images ranges upward of US\$500Bn per year. [2] These costs fall in two categories: resultant (e.g. remediation of reputation, financial loss) and preventative (e.g. cyber insurance, training, infrastructure). However, despite the undoubted cost of untrustworthy images, it is difficult for organizations to commence expensive restructuring of image and data flows to build in trustworthiness when it is unclear how to measure the potential gain. The cost-benefit is difficult to justify in the absence of a clear measurement model. This paper builds upon the authors{\textquoteright} previous research in respect of quantifying the global annual costs of mis- and disinformation by the misuse of media assets, especially images. We suggest the foundations of a cost modelling reference set applicable at varying scales to assist industry, institutions and other organizations with measuring the costs and benefits of image trust interventions, and to inform the development of a robust cost-benefit analysis tool to motivate the uptake of image trust solutions.