Personalized Synthetic Advertising
comissioned deepfake for the Hmm, 2020
I have been invited by the Hmm ︎︎︎ to create a deepfake, that will show how the technology behind deepfakes could be used in our near future. This piece is published in the Hmm dossier on deepfakes ︎︎︎ together with a tutorial describing how the video was made.
What the majority of internet users understand under the term of deepfakes is just the tip of the iceberg. I prefer to call the technology ‘synthetic media’, since the generated AI media can do much more than just face swaps. It has a promising, but not yet realised potential, for various creative industries, advertising, and our everyday communication.
The sensational face-swaps, predominantly used in porn and all kinds of scary manipulation scenarios within a political context, completely overshadow the tremendous potential that lies beneath the surface. Synthetic media is a term for the more general understanding of generated AI media. The deep learning models which are able to generate visuals, sound, and text—and their combination—allows for the existence of a completely false reality. Our eyes or ears would not be able to distinguish this reality from our other experiences. The quality of synthetic media has not yet reached hyper-realistic results. But considering the exponential development in this field, it is just a matter of time until we start seeing the first professional applications of synthetic media in our personal daily lives. One way that we can see this manifesting is in personalised synthetic advertising for example.
Personalised marketing has become a successful strategy to deliver individualised messages and product offerings to customers, based on collected data and its analysis. If you use social media, you’re already being targeted with personalised commercial content. After you’ve browsed Zalando’s webshop, Zalando ads show up on your Facebook page. Or chatting about your period with your friend in Messenger results in YouTube ads for menstrual products. Imagine that these creepy personalised ads go one step further: commercials not only targeted at you, but tailor-made for you. In that YouTube advertisement, the feminine sanitary product is not recommended by a random model, but by the friend you’re chatting with. 🤯
This is very possible with the potential of video and sound synthesis in deepfakes and it’s most likely to happen sooner or later. Personalised synthetic advertising would not only increase the chance that the offered product is what you’re looking for; it might make you more likely to buy it because of the seductive power of familiarity. Seeing your mom’s face popping up from your phone screen, or on the latest smart kitchen device, recommending the best brand of butter while you’re making her famous apple pie recipe might actually be more helpful than annoying. Seeing your friends hanging out in the park with bottles of Heineken might not only make you crave beer, but inspire you to organise a BBQ and socialise more. An interesting question is whether this is purely evil or not. The non-consensual use of someone’s face is definitely crossing the line of privacy, but on the other hand such precedence has already happened. Our society will probably get used, once again, to losing just a little bit more of our private data.
Personalised synthetic advertising could be implemented in familiar places for advertisements, such as social media feeds. But these ads can also pop-up in more unconventional locations. This is what Pavol ︎︎︎ and I explored in our futuristic deepfake video. While being in quarantine ourselves, we took this situation where all our social life moved online as a starting point. The synthetic advertising was placed inside a video call, where it’s triggered by a specific phrase. In our scenario, the phrase “dinner tonight” triggers an automatic fullscreen ad for Uber Eats. Recently, a lot has been written about the data some of the video conferencing apps that we’ve all been using collect. Technically it would be possible to take the call’s video as a source for “deepfaking” the call’s participant and directly applying their face to the personalised advertisement. Our video shows this scenario.
The scenario of the video
It’s Sunday evening at 19:00. We’re in the COVID-19 pandemic. Because of widespread lock-down measures, people have resorted to connecting with each other mainly via video conferencing tools. The two friends in the video, Lenka from Prague and Lisa from Utrecht, call each other via Room, which has become the most popular video conferencing app—chatting about their experiences with the lockdown and other things you talk about with friends.
Watch the video to see what happens next:
The deepfake was made with DeepFaceLab ︎︎︎, currently the most common software used for celebrity face-swaps. Read the tutorial ︎︎︎ in the blogpost at the Hmm’s website to learn how the video was made.
UPDATE: While we were working on this video, the concept of using the realtime footage from a video call was pure speculation. However, in few weeks, an open-source tool for creating basic live facial reenactment deepfakes on Zoom and Skype called Avatarify ︎︎︎ was released on GitHub. The tool is using First Order Motion Model for Image Animation ︎︎︎, which can take a driving video and merge it with one destination image to generate new animated video. This could take us one step closer to real-time video-call deepfakes, but at the moment this needs an extremely powerful graphics card to run (1080 Ti GPU can generate 33 fps, while a better-than-average MacBook would generate only ~1 fps).