From Forbes
Humans Can’t Reliably Tell Real From AI—What That Means For Courts, Claims And Contracts
By Lars Daniel
Generative AI has outpaced the human ability to reliably verify digital files. Every business whose decisions depend on a photo, a video, a voice recording or a document is now operating on a broken assumption.
A new episode of the Berkeley Talks podcast featuring Hany Farid, the UC Berkeley School of Information professor and co-founder of GetReal Security, put the trajectory on paper. Farid has studied image manipulation for two decades. He argues the field has been discussing deepfakes for roughly ten years, but the last two years have been different in kind. Static deepfake files that used to take minutes to generate have given way to fully interactive deepfakes that can hold a live conversation in real time. His research, he says, now puts human detection of AI-generated content only slightly better than chance. Tools once limited to well-resourced state actors are now available to billions of ordinary users.
That is not a social media problem. It is every system that runs on data.
The Problem Is Bigger Than The Internet Feed
Courts admit video as evidence. Insurance claims move on photos, recorded statements and telehealth footage. HR investigations rely on call recordings and chat logs. Bank wire approvals clear on voice confirmations. Corporate fraud exams run on invoices, contracts and signatures. Governance disputes and merger closings turn on meeting recordings and diligence documents.
Every one of those processes makes a decision that assumes somebody, somewhere, can look at the evidence and know whether it is real. Farid's research says that basic assumption has broken.
Farid built his reputation on detection. Pixel-level analysis of images and video for the signs of manipulation, most visibly on fabricated political videos of Barack Obama and other public figures. That is the work GetReal Security now commercializes. Detection examines the file itself for signs of synthesis. Pixel patterns, compression artifacts, generator fingerprints, physics inconsistencies. The output is a probability score. Scoring is a triage function. It helps large platforms and large claim pipelines manage volume. It is because of the volume that solutions like this are necessary...
Hany Farid is a professor in the Department of Electrical Engineering & Computer Sciences and the School of Information at UC Berkeley
