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Fake News: Deepfakes

What are deepfakes?

Deepfakes, or synthetic media, are images, audio clips, or videos that realistically depict something that did not actually occur or exist. What makes deepfakes different than analog or digital fakes is the application of advances in machine learning (ML) and artificial intelligence (AI) to create something that is currently almost -- but soon will be -- indistinguishable from authentic media. (Vales, 2019)

Evaluation tips

  • Source.
    • Consider the source. Who is claiming this video as their own? Where did it come from? What motives might they have for creating or sharing it?
  • Corroboration.
    • Are other credible sources sharing this information? Check multiple sources that cover multiple viewpoints.
  • Blinking.
    • Deepfake videos are often developed using thousands of pictures of a person, and those pictures usually show that person with their eyes open. Is the person in the video not blinking/the blinking does not look natural?
  • Blur.
    • Do you see a blur around the edges of the face or lips? If something comes over the face, like a hand, is there a blur, flicker, or other kind of glitch? Do they appear to have another set of eyebrows or other facial features? These could be a sign that a different face has been laid over the source video. Slow down or pause the video to better check for these cues.
  • Fact-checking.
    • Check to see if credible fact-checking organizations have investigated this video. Sites like Snopes, Politifact, and FactCheck.org can help you debunk a fake video.
  • Confirmation bias.
    • Consider your own biases. Just because a video makes claims that line up with your own views does not mean it has to be true. Check yourself and consult other sources, especially when something seems too outrageous, too good, or too bad to be true.
  • Wait.
    • In today's breaking news environment, news is disseminated quickly. It takes time for disinformation to be caught. Before you react or share, give the experts time to evaluate it. Then check back.

(modified from Louisiana State University, created by Brittany O'Neill) 

Deepfake Video

Examples

Interactive examples
GAN-generated images

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.

"Deep" dive

Articles 
Statements from Watchdog and Other Non-Profit Organizations

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