How AI Might Fake Geographic Data
June 16, 2021
Here is yet another way AI could be used to trick us. The Eurasia Review reports, “Exploring Ways to Detect ‘Deep Fakes’ in Geography.” Researchers at the University of Washington and Oregon State University do not know of any cases where false GIS data has appeared in the wild, but they see it as a strong possibility. In a bid to get ahead of the potential issue, the data scientists created an example of how one might construct such an image and published their findings at Cartography and Geographic Information Science. The Eurasia Review write-up observes:
“Geographic information science (GIS) underlays a whole host of applications, from national defense to autonomous cars, a technology that’s currently under development. Artificial intelligence has made a positive impact on the discipline through the development of Geospatial Artificial Intelligence (GeoAI), which uses machine learning — or artificial intelligence (AI) — to extract and analyze geospatial data. But these same methods could potentially be used to fabricate GPS signals, fake locational information on social media posts, fabricate photographs of geographic environments and more. In short, the same technology that can change the face of an individual in a photo or video can also be used to make fake images of all types, including maps and satellite images. ‘We need to keep all of this in accordance with ethics. But at the same time, we researchers also need to pay attention and find a way to differentiate or identify those fake images,’ Deng said. ‘With a lot of data sets, these images can look real to the human eye.’ To figure out how to detect an artificially constructed image, first you need to construct one.”
We suppose. The researchers suspect they are the first to recognize the potential for GIS fakery, and their paper has received attention around the world. But at what point can one distinguish between warding off a potential scam and giving bad actors ideas? Hard to tell.
The team used the unsupervised deep learning algorithm CycleGAN to introduce parts of Seattle and Beijing into a satellite image of Tacoma, Washington. Curious readers can navigate to the post to view the result, which is convincing to the naked eye. When compared to the actual image using 26 image metrics, however, differences were registered on 20 of them. Details like differences in roof colors, for example, or blurry vs. sharp edges gave it away. We are told to expect more research in this vein so ways of detecting falsified geographic data can be established. The race is on.
Cynthia Murrell, June 16, 2021
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