Fake News: Deepfakes

• 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) 

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.

Articles 

• “Deepfakes: A Looming Crisis for National Security, Democracy and Privacy?” Lawfare, 21 Feb. 2018, https://www.lawfareblog.com/deepfakes-looming-crisis-national-security-democracy-and-privacy.

• Kietzmann, Jan, et al. “Deepfakes: Trick or Treat?” Business Horizons, vol. 63, no. 2, Mar. 2020, pp. 135–46. ScienceDirect, https://doi.org/10.1016/j.bushor.2019.11.006.

• Silbey, Jessica, and Woodrow Hartzog. “The Upside of Deep Fakes Symposium - Truth Decay: Deep Fakes and the Implications for Privacy, National Security, and Democracy.” Maryland Law Review, vol. 78, no. 4, 2019 2018, pp. 960–66, https://heinonline.org/HOL/P?h=hein.journals/mllr78&i=984.

• Vaccari, Cristian, and Andrew Chadwick. “Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News.” Social Media + Society, vol. 6, no. 1, SAGE Publications Ltd, Jan. 2020, p. 2056305120903408. SAGE Journals, https://doi.org/10.1177/2056305120903408.

• Westerlund, Mika. “The Emergence of Deepfake Technology: A Review.” Technology Innovation Management Review, vol. 9, no. 11, Talent First Network, 2019, pp. 40–53, https://doi.org/10.22215/timreview/1282.

Statements from Watchdog and Other Non-Profit Organizations

• “Backgrounder: Deepfakes in 2021.” WITNESS Media Lab, 2 Apr. 2020, https://lab.witness.org/backgrounder-deepfakes-in-2021/.

• Bateman, Jon, and Jon Bateman. “Deepfakes and Synthetic Media in the Financial System: Assessing Threat Scenarios.” Carnegie Endowment for International Peace, https://carnegieendowment.org/2020/07/08/deepfakes-and-synthetic-media-in-financial-system-assessing-threat-scenarios-pub-82237. Accessed 27 Aug. 2021.

• “Voices, Copyrighting and Deepfakes.” IPWatchdog.Com | Patents & Patent Law, 14 Oct. 2020, https://www.ipwatchdog.com/2020/10/14/voices-copyrighting-deepfakes/id=126232/.