AI image fraud will cost $40 billion next year - can these international standards help?

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International standards bodies are stepping up efforts to help provide the tools that end users and companies need to distinguish real images, videos, and other content from deepfakes and AI-generated slop. New standards were recently announced at the AI for Good conference hosted in Geneva under the auspices of the UN.

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A credibility crisis has arisen, and it's only getting worse when it comes to imagery, reaching the point where one can no longer distinguish between actual photos and AI-generated fakes. This has implications across society and businesses, raising doubts about the authenticity of images used in news reports, social media postings, and even photographic evidence in crime scenes. There's a huge financial cost as well: Generative AI could enable fraud losses to reach $40 billion in the US by 2027, up from $12.3 billion in 2023, according to estimates from Deloitte's Center for Financial Services.

With the flood of imagery now appearing on our mobile phones and personal computers, it's difficult, if not impossible, to determine whether photos, videos, or other content are real or AI-generated.

"You now don't know what if something is really fake or not," said Touradj Ebrahimi, professor at the Swiss Federal Institute of Technology. Ebrahimi is leading efforts to address AI fraud and deepfakes, working with leading international standards bodies -- the International Electrotechnical Commission (IEC ), the International Organization for Standardization (ISO) , and the International Telecommunication Union (ITU) -- to develop and evangelize common standards to help users and companies distinguish erroneous AI-generated material from real content. "You need to see metadata and information to find it, to put it in the right context."

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To that end, the IEC and ISO introduced two additions to their JPEG Trust standards to help verify the authenticity of imagery and data. The first JPEG Trust standard, announced last year, is designed to provide a framework for embedding metadata directly into JPEG files in the form of trust indicators.

The two additions to JPEG Trust, now in progress:

According to Ebrahimi, the goal of the JPEG Trust standard is to put verification tools in the hands of end users and is not intended to validate or label imagery or data at the front end when it is created. "Fraudsters will not label their content as AI," he said. "If somebody wants to break the law, they're not going to break the law and follow the other law that says that content needs to be labeled."