'Copyright Traps' Could Tell Writers If an AI Has Scraped Their Work

An anonymous reader quotes a report from MIT Technology Review: Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set. Now they have a new way to prove it: "copyright traps" developed by a team at Imperial College London, pieces of hidden text that allow writers and publishers to subtly mark their work in order to later detect whether it has been used in AI models or not. The idea is similar to traps that have been used by copyright holders throughout history -- strategies like including fake locations on a map or fake words in a dictionary. [...] The code to generate and detect traps is currently available on GitHub, but the team also intends to build a tool that allows people to generate and insert copyright traps themselves. "There is a complete lack of transparency in terms of which content is used to train models, and we think this is preventing finding the right balance [between AI companies and content creators]," says Yves-Alexandre de Montjoye, an associate professor of applied mathematics and computer science at Imperial College London, who led the research. The traps aren't foolproof and can be removed, but De Montjoye says that increasing the number of traps makes it significantly more challenging and resource-intensive to remove. "Whether they can remove all of them or not is an open question, and that's likely to be a bit of a cat-and-mouse game," he says.

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