Where "Deepfake" Began — a Reddit User Called deepfakes, and the 2018 Ban

The word "deepfake" was not coined as the name of a technology. It was the username of one person posting fake videos on Reddit at the end of 2017. On 11 December 2017 Samantha Cole, a reporter at the American technology outlet Motherboard (Vice), wrote about that user, "deepfakes", who had been using machine learning to paste well-known actresses' faces into existing porn videos. Within two months the username had become a noun for the fakes themselves, and the method had become an app anyone could run. On 7 February 2018 Reddit shut the community down.

What a deepfake is and how it works is summarised in the glossary entry for deepfake. This page is about where the word and the technique came from, and what happened in those first few months.

One user and open-source tools

According to Motherboard's first story, deepfakes was not a special-effects studio but an individual, who described himself as a programmer with an interest in machine learning rather than a professional researcher. He declined to give his identity.

By his account the software combined several open-source libraries, such as Keras with a TensorFlow backend, and he gathered the faces from Google image search, stock photos and YouTube videos. "I just found a clever way to do face-swap," he said. "With hundreds of face images, I can easily generate millions of distorted images to train the network," he went on. "After that if I feed the network someone else's face, the network will think it's just another distorted image and try to make it look like the training face."

Researchers later described this original network as one encoder and two decoders. The two people's faces are learned at the same time, each reconstructed through the shared encoder and that person's own decoder. Because the encoder learns to map both faces into a common representation, encoding one person's face and decoding it with the other person's decoder swaps the face while keeping the expression and pose.

The AI researcher Alex Champandard told Motherboard that a decent consumer graphics card could process the effect in hours, and a CPU would do it in days. "This is no longer rocket science," he said.

The results were not perfect. The story noted faces that failed to track and the original image peeking through around the face. At a glance, though, they were believable.

A noun, then an app

In a follow-up on 24 January 2018, Cole reported how fast things had moved.

Within the community, the follow-up said, "deepfake" was already a noun for the kind of neural-network-generated fake video its namesake had pioneered. A web-archive snapshot of r/deepfakes from 27 January 2018 shows the community as created by deepfakes himself, two months earlier. Dictionaries describe the word as a blend of the "deep" of deep learning and "fake".

The other turning point was FakeApp. Another user, deepfakeapp, packaged the deepfakes method into an application meant for people with no programming background. The community wiki described it as "a community-developed desktop app to run the deepfakes algorithm without installing Python, Tensorflow, etc.", needing only a good GPU with CUDA support; users without one could rent cloud GPUs. Done correctly, the whole process, from extracting the data to converting the video frame by frame, took about eight to 12 hours.

Peter Eckersley, chief computer scientist at the Electronic Frontier Foundation, had told Cole in early January that the technology was on the cusp of becoming easy and widespread, and that the fakes' detectability was "not going to stay true for more than a year or two." The follow-up observed that it had barely lasted two months.

7 February 2018: Reddit's ban

The services around Reddit moved first. In the preceding week, Motherboard reported, Discord, Gfycat, Pornhub and Twitter had all banned deepfakes as a form of involuntary pornography. Because the videos themselves were hosted on outside services such as Gfycat, some members of the subreddit assumed it would not be banned.

On 7 February Reddit's administrators announced in r/announcements that a rule which had combined involuntary pornography and sexual or suggestive content involving minors was being split into two distinct rules. The new wording, as quoted by TechCrunch, prohibits nude or sexual images or video of a person apparently created or posted without their permission, "including depictions that have been faked", and also bars posting someone's images for the specific purpose of faking explicit content.

r/deepfakes was closed the same day. Motherboard put its subscribers at nearly 90,000 and TechCrunch at more than 92,000. Older subreddits such as r/CelebFakes, which had shared Photoshopped celebrity fakes for years, were shut too, while non-pornographic offshoots such as r/SFWdeepfakes stayed up. The makers, Motherboard noted, were already moving to Discord channels and their own websites; the ban closed a gathering place, not the technique.

From scandal to research field

After this, deepfakes became an academic subject. A 2020 survey paper notes that BuzzFeed's 2018 fake video of former President Obama was made with FakeApp, and counts papers on the subject rising from 3 in 2017 to more than 250 in 2018–20. Open-source tools such as DeepFaceLab kept refining the original Reddit network.

In a February 2020 explainer, the US Government Accountability Office wrote that deepfakes could influence elections and erode trust but so far had mainly been used for non-consensual pornography — the same skew that Reddit had seen in the first few months.

Before the law caught up

In Japan, three men were arrested in October 2020 on suspicion of defamation for posting videos in which porn performers' faces had been replaced with those of entertainers; the Ryukyu Shimpo described it as the country's first arrest over a deepfake video.

In the United States the TAKE IT DOWN Act became law on 19 May 2025. It makes publishing non-consensual intimate images a federal crime, explicitly including what it calls a "digital forgery", an intimate image created with software, machine learning or AI, and requires platforms to remove such an image within 48 hours of a valid request from the victim, with one year from enactment to set up the process. What Reddit had written into its rules in 2018 became federal law seven years later.

The problem did not go away. On 12 January 2026 Ofcom, the UK's online safety regulator, opened a formal investigation into X after reports that its AI tool Grok had been used to generate and share sexualised images. In 2017 the tool for making such fakes was home-made code passed around by one user; by 2026 it was a feature built into one of the largest platforms.

Other pages on this site follow deepfakes beyond sexual imagery. In January 2024, just before a US primary, voters received robocalls in a fake Biden voice, and the same month in Hong Kong a video call full of fake executives cost a company about HK$200 million. On the regulatory side, the EU AI Act defines deep fakes and requires those who deploy AI to generate or manipulate them to disclose that the content is artificial.

See also the AI and society timeline.