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The first time Hany Farid heard the term “deepfake” was in 2017. At the time he was part of a Defense Advanced Research Projects Agency project to develop tools for authenticating digital media. The project was not about AI at all. But suddenly, AI was all anybody was talking about.⁠ ⁠ The AI of the time needed thousands of images of someone’s face to create a convincing fake version. But it was clear things were going to progress quickly, Farid says. One reason was that new technologies always get faster, cheaper and more ubiquitous, he says. The other reason was sex.⁠ ⁠ The term “deepfake” comes from a Reddit user with the handle “deepfakes” who created a subreddit dedicated to nonconsensual sexual imagery—pornographic videos in which a participant’s face is replaced with the AI-generated likeness of someone else. The vast amount of harmful AI images on the internet today are still “disproportionately on the nonconsensual, fake nude side rather than on the [disinformation] side,” says Alexios Mantzarlis, a misinformation researcher who used to work on AI at Google. And, Farid adds, “Once you get sex involved on the internet, new technologies spread fast.”⁠ ⁠ By now, billions of people have access to technology that can produce photorealistic images in a matter of seconds based on the most outlandish prompts. In the beginning, AI images were often easy to detect because they lacked some of the subtle statistical signatures found in real photographs, such as sensor noise or lens artifacts.⁠ ⁠ But image generators have become increasingly good at learning and reproducing those patterns, and now they can add realistic-looking noise and imperfections. And because AI images are created from scratch rather than altered in conventional editing software, some of the detection methods Farid first developed—based on pixel-level statistical relationships left behind by manipulation—no longer work well, if at all.⁠ ⁠ To learn more, click the link in our bio.⁠ ⁠ IMAGE CREDITS: HANY FARID