Media & Enterprise
A trust and safety team is not short of judgement. It is short of time. FaceOff checks images and video at the volume a platform actually receives, shows how the accounts behind them connect, and returns every finding with its reasoning attached so a reviewer decides and the platform can explain that decision afterwards
Overview
Give trust and safety teams a way to check images and video at volume, and to see how accounts posting them connect to each other.
Moderation is rarely hard because a single case is hard. It is hard because there are far too many cases and no way to tell in advance which ones deserve a person. The most serious material looks exactly like the least serious until somebody checks, so a queue ordered by arrival time spends its best attention in the wrong places.
Synthetic media made that worse in two ways at once. Convincing images and video can be produced faster than anybody can review them, and a fake of a real person is almost never posted only once. It reappears across accounts and across platforms, so a report that looks like a single incident is usually one fragment of something larger that nobody has joined up.
The checks below run at the volume a platform receives, and each returns a graded finding with the reasoning behind it. That turns a flat queue into an ordered one and gives a reviewer something to act on and to explain later to the user affected, to an appeals process or to a regulator.
What you deploy
These are the products trust and safety teams deploy most often, covering what was posted, where else the same material appears and how the accounts behind it relate to one another.
Key capabilities
What this sector asks for most often. Each one runs at the moment a decision is being made, and each returns a result a reviewer can explain to somebody else.
Material is checked for signs of editing or AI generation without a person needing to open it first, and every result comes back graded. A review queue can then be ordered by what most needs human attention rather than by what happened to arrive first.
When somebody is impersonated, the same material usually surfaces in many places. Suspected fakes of a named person are found across online platforms and each match is checked against the real person, so a single report becomes an accurate measure of how far the problem has actually spread.
Public information about the accounts involved is drawn into one structured view, including networks acting together and profiles that were built rather than grown. A campaign can then be understood as a single thing instead of a hundred unrelated reports.
Images and video can be checked while they are still on screen, which is what a moderator, a journalist or a researcher needs when the question is about something in front of them right now rather than something already sitting in a queue.
A finding does not stop at a verdict. On an image it shows the areas that look altered, and on a long video it points to the moments worth watching, so a reviewer spends a minute where they would otherwise have spent twenty.
Nothing is removed automatically. Every finding carries the evidence behind it and is kept as a record, so an enforcement decision can be justified to the user affected, to your own appeals process and to a regulator asking how it was reached.
Where it is used
Every entry names a product on this site and describes it the way its own page does.
DeepImageGuard checks images for signs of AI generation or editing and highlights the areas that look manipulated.
DeepVideoGuard checks video for face swaps and generated footage and points to the moments worth reviewing.
DeepFakeFinder finds and verifies suspected deepfakes of a person across online platforms.
SocialIntel Analyzer turns public information into a structured view, and Identity Intelligence Engine draws together records that may belong to one person.
Common questions
Bring your own footage. We will run it through the Adaptive Cognito Engine and walk you through the findings.
