Image: Egor Zakharov, Samsung AI Center (Moscow)
May 29
Flash back to July 2017. Brussels, Belgium. At the foreign news desk of VRT NEWS — Belgium’s public broadcaster, where I work — I watch an online video of president Barack Obama who warns viewers about the potential dangers of faked video. The video itself is fake, too, but very convincing. It is generated by researchers at the University of Washington who are using artificial intelligence tools to precisely model how the president moves his mouth when he speaks.
I’m alarmed. I start to realize that we’ve only seen the beginning of fake news. Instead of being mostly textual, we are now entering into a new fake news era of digitally altered video and audio, and this could even further erode and undermine public trust in journalism and harm democracies. I decide to apply for the John S. Knight (JSK) Journalism Fellowship at Stanford University, hoping that I can study this phenomenon and contribute to a possible solution.
Flash forward to May 2019. Stanford University. The phenomenon “deepfake,” a portmanteau of “deep learning” and “fakes,” is now widespread. The term covers not only videos and images, but also audio files generated or altered with the help of artificial intelligence, with the intent of deceiving an audience into thinking that they are real.
The word “deepfakes” originated in December 2017 with an anonymous user on the online platform Reddit who called himself “deepfakes.” He applied deep-learning algorithms to digitally superimpose faces of celebrities on actors in pornographic content. Ultimately, he got banned from Reddit. But a wave of copycats replaced him on other platforms. Experts believe there are now about 10,000 deepfake videos circulating online, and the number is growing.
As a John S. Knight Journalism Fellow at Stanford University, I approached my deepfake challenge by first studying artificial intelligence, its impact on journalism and how we can integrate these techniques in our newsrooms. This strategy gave me a chance to tap into a wealth of resources on campus, take classes on AI and meet with researchers and students. Over time, I pulled together a group of people who are also concerned about deepfakes, and who wanted to learn from each other.
Deepfake Research Team meeting in the JSK Garage (Stanford University)
I invited deep learning experts, computer science students and visiting journalists on campus to discuss this topic in the “JSK Garage,” our seminar room at Stanford. These irregular meetings grew into a slightly more formal group that we called the Deepfake Research Team (DRT), and we created a Slack workspace to share our work. Our goal: to raise awareness, foster interdisciplinary collaboration and exchange ideas about solutions to stop deepfakes.
While solutions to combating deepfakes are still far off, I’d like to share the most important lessons I have learned so far.
Compared to two years ago, the techniques for developing “synthetic media” are becoming better, more common and easier to use. Anyone can now create hyper-realistic deepfakes, even without much knowledge of machine learning. A search on Github for free software to develop deepfakes shows over 100 repository results. Most of them are a variant of the technique called face swap, but there are several different approaches to manipulating video, audio and images.
The deepfake technology is using generative adversarial networks (GANs) which are trained to replicate patterns, such as the face of a president, and gradually improves the realism of the synthetically generated faces. Basically, it works like a cat-and-mouse game between two neural networks.
One network, called “the generator,” is producing the fake video based on training data (real images), and the other network, “the discriminator,” is trying to distinguish between the real images and the fake video. This iterative process continues until the generator is able to fool the discriminator into thinking that the footage is real.
To create a deepfake video, it is crucial to have a powerful video card (GPU). The better result you want, the longer that you’ll have to run the computing process. If you don’t have the time or your GPU is poor, you can now simply order a deepfake video online. On YouTube, for example, it’s easy to find people who are offering their services for as little as $20 per request.