Deepfakes, or AI-generated videos that take a person in an existing video and replace them with someone else's likeness, are multiplying at an accelerating rate. According to startup Deeptrace, the number of deepfakes on the web increased 330% from October 2019 to June 2020, reaching over 50,000 at their peak. That's troubling not only because these fakes might be used to sway opinion during an election or implicate a person in a crime, but because they've already been abused to generate pornographic material of actors and defraud a major energy producer. While much of the discussion to date around deepfakes has focused on social media, pornography, and fraud, it's worth noting that deepfakes pose a threat to people portrayed in manipulated videos and their circle of trust. As a result, deepfakes also represent an existential threat to businesses, particularly in industries that depend on digital media to make important decisions.

AI is constantly in the news these days, identifying prospects for the technology doing both good and bad. One topic that's generating a lot of buzz is the use of AI for creating "deepfakes," a term originally coined in 2017. Deepfakes uses neural networks to combine and superimpose existing images and videos onto source images or videos using a deep learning technique known as generative adversarial networks (GANs). Three of the most common deepfakes techniques are known as "lip-sync," "face swap," and "puppet-master." These techniques, however, can create a disconnect that may be uncovered by a clever algorithm as a way to combat deepfakes.