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Deepfake geography? When geospatial data encounter Artificial Intelligence
Topics: Geographic Information Science and Systems
, Digital Geographies
, Remote Sensing
Keywords: deepfake, GeoAI, AI, deep learning, humanistic geography, fake geography, disinformation, misinformation, post-truth Session Type: Virtual Paper Abstract Day: Tuesday Session Start / End Time: 3/1/2022 08:00 AM (Eastern Time (US & Canada)) - 3/1/2022 09:20 AM (Eastern Time (US & Canada)) Room: Virtual 35
Authors:
Bo Zhao, University of Washington
Shaozeng Zhang, Oregon State University
Chunxue Xu, Oregon State University
Yifan Sun, University of Washington
Chenbin Deng, Binghamton University
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Abstract
The developing convergence of Artificial Intelligence and GIScience has raised a concern on the emergence of deep fake geography and its potentials in transforming human perception of the geographic world. Situating fake geography under the context of modern cartography and GIScience, this paper presents an empirical study to dissect the algorithmic mechanism of falsifying satellite images with non-existent landscape features. To demonstrate our pioneering attempt at deep fake detection, a robust approach is then proposed and evaluated. Our proactive study warns of the emergence and proliferation of deep fakes in geography just as “lies” in maps. We suggest timely detections of deep fakes in geospatial data and proper coping strategies when necessary. More importantly, it is encouraged to cultivate a critical geospatial data literacy and thus to understand the multi-faceted impacts of deep fake geography on individuals and human society.
Deepfake geography? When geospatial data encounter Artificial Intelligence
