Beyond Deep Fakes

Communications of the ACM 

Within the next five years, the way we work, live, play, and learn will be changed by digital humans (chatbots and avatars with very realistic human faces). Digital humans are already gaining popularity as social media influencers, and they will soon evolve into digital sales assistants, fashion advisers, and personal shoppers able to model how customers will look and move in the latest ensembles. Digital humans will become central to the multibillion-dollar fashion industry, as social media is further integrated into the retail customer experience. Digital humans will also help in healthcare, enabling medical students and social workers to develop better interview skills for patients in sensitive clinical settings. They will allow people, especially those with mental health challenges, to rehearse for job interviews. They will help keep elderly people connected to their communities and respectfully monitored so they can remain in their homes longer. They will provide a human face for personalized advice, support, and training--and do it at scale. This has become possible with the advent of cost-effective, highly realistic, personalized interactive digital agents and avatars sporting high-fidelity facial simulations powered by advances in both real-time neural rendering (NR) and low-latency computing. NR refers to the use of machine-learning (ML) techniques to generate digital faces or face replacements in video.17 NR rose to prominence with the advent of so-called "deep fakes"--the replacement of someone's face in videos with an NR-generated face of remarkable realism. The term originates from the name of a Reddit user (/u/deepfakes), a ML engineer who posted the original deep fake auto-encoder. Often used for satire, deep fakes can be harmful, presenting novel ethical issues. The best-known examples involve deep fakes of celebrities, a form of face "hijacking" whereby publicly available videos of a person are used to train an ML program that overlays the source person's face onto existing video footage; this technique was originally used in pornographic material.

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