Why deepfake detection is becoming trust infrastructure For many, the word “deepfake” still has a twinge of novelty about it – a hint of science fiction. But the cute era of generative AI is over, as criminals leverage it to create fake identities that are flooding the online world, causing major headaches for institutions and enterprises, and driving the market for deepfake detection. It’s hard to know what’s real anymore The continuing digitization of society has created a huge target range for fraudsters, and the emergence of cheap, easily accessible generative AI tools into the general populace has given them a weapon more potent than any they’ve had before. Any screen is a potential attack surface; phone calls are becoming archaic technology ruined by fraud and voice scams. Tactics target the whole range of the citizenry, from elderly people in care homes to high-powered CEOs. Hiring pipelines are under siege from fake candidates, executives are being impersonated, and the images we once trusted can no longer be taken at face value. According to a recent UN report, “generative artificial intelligence has dramatically reduced the technical barriers and workforce requirements for conducting sophisticated fraud, enabling criminal operators to generate convincing phishing content, deploy real-time deepfake video and voice during live calls, and target victims across dozens of languages simultaneously.” AI-assisted fraud is a global mega-industry Nor is the realism of deepfakes the only problem. Generative AI has also increased efficiency, enabling fraud at industrial scale. Fraud-as-a-service has emerged as a model, selling fake identities and fraud support on a subscription basis. The recently published report from Biometric Update and Goode Intelligence, Deepfake Fraud Detection Market 2026: Securing Identity in the AI Era, points to an emerging truth: deepfake detection is becoming a key part of overall trust architecture. Businesses in industries