Each month, Cyabra’s experts share the articles, reports, and investigations shaping the conversation around misinformation, narrative intelligence and deepfakes. This October, from AI-generated ads flooding the US midterms to new research on detecting deepfakes and the people they harm, the conversation is moving from whether synthetic content will shape public trust to how quickly institutions, researchers, and platforms can respond.
With threats to midterms, states say Trump administration isn’t helping
Washington Post
“We’re in the dark when it comes to any sort of intelligence” on foreign adversaries… “We’re not being informed if there are problems.”
– Deidre Henderson, Utah’s Republican Lt. Gov.
Detecting and countering misuse of AI: September 2026
Anthropic
“AI has inverted the cost back onto defenders… capable adversaries can ‘close the loop,’ bypassing traditional security detections faster than defenders can develop and deploy them.”
$137 Million in A.I.-Generated Ads Flood the Midterms
New York Times
“I think what we’re seeing with A.I. has surprised even us… Some of the ways in which A.I. is used, honestly, if there wasn’t a disclaimer on the ad, it would be hard to know that A.I. was there.”
– Erika Franklin Fowler, Wesleyan University, co-director of the Wesleyan Media Project
Two UOW experts explain how to spot AI-generated content and why the answer is rarely in the picture alone.
University of Wollongong, Australia
“Children need to assume that any content and media they encounter online could be AI generated… Expecting individuals to navigate AI-generated content without guardrails is an impossible task.”
– Dr Karley Beckman, Senior Lecturer in Digital Technologies
Celebrity victims of deepfake sexual abuse face unique victim-blaming and barriers to justice
University College Cork, Ireland
“This research shows that the harm of deepfake sexual abuse goes far beyond the creation of a single image.”
– Professor Gillian Murphy, University College Cork
AI deepfakes are getting more realistic. CU scientists developed a better way to spot them
University of Colorado Boulder
“We only had to look at data from one out of every hundred frames, instead of every frame… By looking into the system from another perspective, you can actually solve the problem.”
– Prof. Sangtae Ha, Computer Science Professor at CU Boulder
