Another Side of the AI Boom: Detecting What AI Makes

Andrey Doronichev was alarmed last year when he saw a video on social media that appeared to show the president of Ukraine surrendering to Russia.

The video was quickly debunked as a synthetically generated deepfake, but to Mr. Doronichev, it was a worrying portent. This year, his fears crept closer to reality, as companies began competing to enhance and release artificial intelligence technology despite the havoc it could cause.

Generative A.I. is now available to anyone, and it’s increasingly capable of fooling people with text, audio, images and videos that seem to be conceived and captured by humans. The risk of societal gullibility has set off concerns about disinformation, job loss, discrimination, privacy and broad dystopia.

For entrepreneurs like Mr. Doronichev, it has also become a business opportunity. More than a dozen companies now offer tools to identify whether something was made with artificial intelligence, with names like Sensity AI (deepfake detection), Fictitious.AI (plagiarism detection) and Originality.AI (also plagiarism).

Mr. Doronichev, a Russian native, founded a company in San Francisco, Optic, to help identify synthetic or spoofed material — to be, in his words, “an airport X-ray machine for digital content.”

In March, it unveiled a website where users can check images to see if they were made by actual photographs or artificial intelligence. It is working on other services to verify video and audio.

“Content authenticity is going to become a major problem for society as a whole,” said Mr. Doronichev, who was an investor for a face-swapping app called Reface. “We’re entering the age of cheap fakes.” Since it doesn’t cost much to produce fake content, he said, it can be done at scale.

The overall generative A.I. market is expected to exceed $109 billion by 2030, growing 35.6 percent a year on average until then, according to the market research firm Grand View Research. Businesses focused on detecting the technology are a growing part of the industry.

Months after being created by a Princeton University student, GPTZero claims that more than a million people have used its program to suss out computer-generated text. Reality Defender was one of 414 companies chosen from 17,000 applications to be funded by the start-up accelerator Y Combinator this winter.

CopyLeaks raised $7.75 million last year in part to expand its anti-plagiarism services for schools and universities to detect artificial intelligence in students’ work. Sentinel, whose founders specialized in cybersecurity and information warfare for the British Royal Navy and the North Atlantic Treaty Organization, closed a $1.5 million seed round in 2020 that was backed in part by one of Skype’s founding engineers to help protect democracies against deepfakes and other malicious synthetic media.

Major tech companies are also involved: Intel’s FakeCatcher claims to be able to identify deepfake videos with 96 percent accuracy, in part by analyzing pixels for subtle signs of blood flow in human faces.

Within the federal government, the Defense Advanced Research Projects Agency plans to spend nearly $30 million this year to run Semantic Forensics, a program that develops algorithms to automatically detect deepfakes and determine whether they are malicious.

Even OpenAI, which turbocharged the A.I. boom when it released its ChatGPT tool late last year, is working on detection services. The company, based in San Francisco, debuted a free tool in January to help distinguish between text composed by a human and text written by artificial intelligence.

OpenAI stressed that while the tool was an improvement on past iterations, it was still “not fully reliable.” The tool correctly identified 26 percent of artificially generated text but falsely flagged 9 percent of text from humans as computer generated.

The OpenAI tool is burdened with common flaws in detection programs: It struggles with short texts and writing that is not in English. In educational settings, plagiarism-detection tools such as TurnItIn have been accused of inaccurately classifying essays written by students as being generated by chatbots.

Detection tools inherently lag behind the generative technology they are trying to detect. By the time a defense system is able to recognize the work of a new chatbot or image generator, like Google Bard or Midjourney, developers are already coming up with a new iteration that can evade that defense. The situation has been described as an arms race or a virus-antivirus relationship where one begets the other, over and over.

“When Midjourney releases Midjourney 5, my starter gun goes off, and I start working to catch up — and while I’m doing that, they’re working on Midjourney 6,” said Hany Farid, a professor of computer science at the University of California, Berkeley, who specializes in digital forensics and is also involved in the A.I. detection industry. “It’s an inherently adversarial game where as I work on the detector, somebody is building a better mousetrap, a better synthesizer.”

Despite the constant catch-up, many companies have seen demand for A.I. detection from schools and educators, said Joshua Tucker, a professor of politics at New York University and a co-director of its Center for Social Media and Politics. He questioned whether a similar market would emerge ahead of the 2024 election.

“Will we see a sort of parallel wing of these companies developing to help protect political candidates so they can know when they’re being sort of targeted by these kinds of things,” he said.

Experts said that synthetically generated video was still fairly clunky and easy to identify, but that audio cloning and image-crafting were both highly advanced. Separating real from fake will require digital forensics tactics such as reverse image searches and IP address tracking.

Available detection programs are being tested with examples that are “very different than going into the wild, where images that have been making the rounds and have gotten modified and cropped and downsized and transcoded and annotated and God knows what else has happened to them,” Mr. Farid said.

“That laundering of content makes this a hard task,” he added.

The Content Authenticity Initiative, a consortium of 1,000 companies and organizations, is one group trying to make generative technology obvious from the outset. (It’s led by Adobe, with members such as The New York Times and artificial intelligence players like Stability A.I.) Rather than piece together the origin of an image or a video later in its life cycle, the group is trying to establish standards that will apply traceable credentials to digital work upon creation.

Adobe said last week that its generative technology Firefly would be integrated into Google Bard, where it will attach “nutrition labels” to the content it produces, including the date an image was made and the digital tools used to create it.

Jeff Sakasegawa, the trust and safety architect at Persona, a company that helps verify consumer identity, said the challenges raised by artificial intelligence had only begun.

“The wave is building momentum,” he said. “It’s heading toward the shore. I don’t think it’s crashed yet.”