The Pentagon is getting better at catching deepfakes. DARPA’s Semantic Forensics program, the Defense Innovation Unit’s prototype contract with Hive, and a growing stack of detection tools all point the same way. The Department of Defense has decided that the disinformation fight is a detection problem.
They are wrong — not about deepfakes being a threat, but about where the real threat lives.
The detection tools the DoD is funding were built for a world where adversaries generate fake images, fake video and fake audio and then try to pass it off as real. That world exists. But it is the easy version of the problem. The harder version, the one showing up in research but not in budgets, is upstream. Adversaries are poisoning the AI models that defense analysts, intelligence platforms, and policymakers rely on to sort real from fake in the first place.
It is unclear whether the DoD has already encountered this problem in its own systems. But civilian researchers have documented it repeatedly. The Atlantic Council’s Digital Forensic Research Lab and the UK AI Security Institute have both published evidence of adversarial content embedded in the training data that feeds widely used AI models.
