Maps have always told white lies. To paraphrase the words of Mark Monmonier in his classic book How to Lie with Maps, representing a curving Earth on a flat plane necessarily involves some distortion. Fake satellite images are different: the apparent authority of a satellite photograph can make us forget that they have the same vulnerabilities as any other piece of data. We know fake satellite images exist. The question is whether we can detect them – and how reliably. A new machine-learning algorithm can spot a particular kind of faked satellite imagery with 94 percent accuracy, but data literacy is the best way to sort reliable images from unreliable ones. Generative Adversarial Networks (GANs) are frequently used to create convincing deep-fake media. Sometimes the results are less than convincing, as with the recent fake videos of Ukrainian president Volodymyr Zelensky.
Using Cycle-GANs, researchers created fake maps of Tacoma, a city in the US state of Washington. The fake maps included some features of Seattle, Washington, and the Chinese city of Beijing.
GANs consist of a generator network and a discriminator network, which work in tandem through rounds of tuning until they have produced a convincing fake according to the characteristics of the data they are aiming to simulate.
