![]() "This is always going to be a game of cat and mouse," Schick said. ![]() The result is an output that increasingly improves over time. As the two networks work against each other in a sort of competition, they hone each other's capability. The generator attempts to perfect an output, images of faces, for example, while the discriminator tries to determine if the new images had been created artificially. The two networks - one a "generator," the other a "discriminator" - are then pitted against each other. GANs consist of two neural networks, which are a series of algorithms that find relationships in a data set, like a collection of photos of faces. That is because the technology that makes deepfakes possible is a type of deep learning called generative adversarial networks (GANs). The first is to build technology that can determine if a video has been manipulated - a task that is harder than it seems. "Because if we don't, then any idea of a shared reality or a shared objective reality is absolutely going to disappear." Looking for truth - how to authenticate real videosīut how can people determine if a video has been faked? Schick said there are two ways of approaching the problem. "We really have to think about how do we inbuild some kind of security so that we can ensure that there is some degree of trust with all the digital content that we interact with on a day-to-day basis," Schick said. But if jurors cannot agree on their authenticity, the same video could exonerate someone - or send them to prison for years. Videos, she pointed out, are currently compelling evidence in a court of law. ![]() She told 60 Minutes this "liar's dividend" concept carries the potential to erode the information ecosystem. Nina Schick, a political scientist and technology consultant, wrote the book Deepfakes. "We conclude that no one in the video is really one person but rather they are all digital composites of two or more real people to form completely new digital persons using deepfake technology," Heartstrong wrote in the report, according to the Daily Beast. The false report alleged that Floyd had been long-dead. One public example of this occurred last year, when Winnie Heartstrong, a Republican Congressional candidate, released a 23-page report claiming that the video of George Floyd's murder was actually a deepfake. That is the so-called "dividend" paid out to the liar. ![]() "Put simply: a skeptical public will be primed to doubt the authenticity of real audio and video evidence."Īs the public learns more about the threats posed by deepfakes, efforts to debunk lies can instead seem to legitimize disinformation, as a portion of the audience believes there must be some truth to the fraudulent claim. "As the public becomes more aware of the idea that video and audio can be convincingly faked, some will try to escape accountability for their actions by denouncing authentic video and audio as deepfakes," they wrote. ![]() It is a paradox Chesney and Citron call the "Liar's Dividend." So, if any video or audio can be faked, then anyone can dismiss the truth by claiming it is synthetic media. As manipulated videos pervade the internet, it may become progressively harder to separate fact from fiction. But deepfakes can distort the truth in another insidious way. The primary threat of deepfakes stems from people becoming convinced that something fictional really occurred. ![]()
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