While seeing long-dead artists eating modern food is fun, this hides a darker problem about authenticity on the web. With heightened concerns about the "fake news" label being used to undermine information, deepfakes are set to accelerate a crisis in trust.
What Are Deepfakes?
Deepfakes use deep learning—specifically generative adversarial networks (GANs)—to create synthetic media. The technology can swap faces in videos, clone voices, and generate entirely fictional footage that looks completely real.
What started as a novelty has become a serious threat. The same technology that creates entertaining face swaps can produce convincing political disinformation, financial fraud, and personal harassment.
The Trust Implications
The most insidious effect of deepfakes isn't the fakes themselves—it's the uncertainty they create. When any video might be fake, real evidence becomes deniable. "That's a deepfake" becomes the new "fake news."
This erosion of trust affects everything from journalism to justice. How do we prosecute crimes when video evidence can be dismissed as synthetic? How do we inform voters when political footage might be fabricated?
"The crisis isn't just about fake content—it's about losing faith in real content."
What Can We Do?
Detection technology is racing to keep up with generation technology. We need media literacy education, provenance tracking systems, and robust verification processes. But fundamentally, we need to recalibrate our relationship with visual media.
The era of "seeing is believing" is ending. What replaces it will define how we maintain trust in an age of synthetic reality.