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For instance, a GAN can look at thousands of photos of Barack Obama, and then produce a new photo that approximates those photos without being an exact copy of any one of them, as if it has come up with an entirely new portrait of the former president not yet taken. Anyone with access to this technology — from state-sanctioned propagandists to trolls — would be able to skew information, manipulate beliefs, and in so doing, push ideologically opposed online communities deeper into their own subjective realities. At the same time, Hwang acknowledges that as deep fakes become more realistic and easier to produce in the coming years, they could usher in an era of forgery qualitatively different from what we have seen before. Although Farid is locked in this technical cat-and-mouse game with deep fake creators, he is aware that the solution does not lie in new technology alone. According to Danielle Citron, we are already beginning to see the social ramifications of this epistemic decay.

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As well as considering the threat to privacy and national security, both scholars became increasingly concerned that the proliferation of deep fakes could catastrophically erode trust between different factions of society in an already polarized political climate.

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You thought fake news was bad? Deep fakes are where truth goes to die

Relying on forensic detection alone to combat deep fakes is becoming less viable, he believes, due to the rate at which machine learning techniques can circumvent them. This article has been amended to clarify that though sp. GANs might also be used to generate new audio from existing audio, or new text from existing text — it is a multi-use technology. They could be weaponized in ways that weaken the fabric of democratic society itself.

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