Stream Engine Delivers AI Deepfake Detection Analytic to the Air Force Research Laboratory, Reaching Test-Ready Status in Weeks

Built from the ground up under a Cooperative Research and Development Agreement with AFRL's Information Directorate, Stream Engine's semantic analytic is now integrated into AFRL's newly operational test and evaluation testbed, and AFRL has recommended the collaboration continue

ROME, N.Y., Nov. 3, 2026 /PRNewswire/ -- Stream Engine, a provider of decision intelligence for national security, today announced that it has developed and delivered a containerized, semantic-based deepfake detection analytic to the Air Force Research Laboratory (AFRL) Information Directorate under a Cooperative Research and Development Agreement (CRADA). According to AFRL's CRADA annual report (Case No. AFRL-2026-4061), the analytic has been integrated into AFRL's new Test and Evaluation (T&E) testbed, the testbed is fully operational, and the system was ready for formal T&E as of June 2026. AFRL recommended that the CRADA effort continue.

The CRADA was executed on June 6, 2025, for a 12-month term, with a primary technical objective of integrating and evaluating semantic-based deepfake detection technologies. At the project kickoff, the Joint Work Plan was restructured to maximize value for both parties: Stream Engine would build a semantic-based deepfake detection analytic from the ground up, while AFRL used the integration to stress-test and mature its newly established T&E testbed.

From language models to likelihood scores

Stream Engine applied its large language model (LLM) expertise in text analysis to build a two-stage detection pipeline. First, a semantic feature extraction model analyzes text along two dimensions: grammatical and syntactic anomalies, and semantic inconsistencies across the text. A neural network classifier then combines these features into likelihood scores designed to separate machine-generated text from human writing that is simply poorly written. Surface-level fluency cues alone cannot reliably draw that line.

Stream Engine packaged the analytic as a container built on an AFRL-engineered Docker template, which allowed secure deployment into the T&E testbed. AFRL supplied synthetic and manipulated datasets for training, along with a custom software environment for integration and testing. AFRL's report cites "improved technology or software" as a technology transfer benefit to the Air Force.

Test-ready despite headwinds

The team reached test-ready status while working through a facility power outage, a six-week federal government shutdown, and the approvals required to access the testbed. AFRL reported that the T&E environment was successfully configured in June 2026 and that the testbed is ready for use in this and future programs.

No federal funds were exchanged under the agreement. The parties estimated Stream Engine's contributions over the 12-month period at more than $1 million, including personnel, intellectual property, supplies and equipment.

"We were asked to take our language intelligence and point it at one of the hardest problems in national security: telling whether content was written by a person or manufactured by a machine," said Brian Kim, co-founder and CEO of Stream Engine. "Our team built that analytic from the ground up, delivered it in a deployable container, and now it sits in AFRL's testbed ready for formal evaluation. That is the pace this mission demands, and it reflects what Stream Engine is built to do: turn signals into decision intelligence for the people protecting the country."

With the testbed configured, the next phase of the collaboration is formal test and evaluation of the analytic.

About Stream Engine

Stream Engine delivers decision intelligence for national security. Its AI platform, built on purpose-trained Special Purpose Models, turns text, images, audio and video from publicly available and mission sources into prioritized intelligence that helps analysts, investigators and security teams act faster. Stream Engine supports defense and intelligence organizations with OSINT, narrative intelligence and deepfake detection; equips law enforcement agencies to triage high volumes of cybercrime and child-exploitation tips and accelerate investigations; and provides protective intelligence for major sporting and live events. Stream Engine is committed to AI for Good®. Learn more at stream-engine.io.

Media Contact

David Wilson, Partner and Chief Growth Officer, Stream Engine
press@stream-engine.io 

Source and Clearance

Facts about the collaboration are drawn from the AFRL Information Directorate CRADA Annual Report, "Assessment and Enhancements of Stream Engine Capabilities" (Technical Report), reporting period June 6, 2025 to June 6, 2026.

Originator Reference Number: RFG26-165
Case Number: AFRL-2026-4061
Case Reviewer: Keith Klug

Approved for public release; distribution is unlimited.

Reference to the Air Force Research Laboratory does not constitute or imply its endorsement of Stream Engine or its products. The views expressed are those of Stream Engine and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. Government.

SOURCE Stream Engine