From detection to decisions: a media review workflow

Connect analysis to clear review steps, preserve uncertainty, and give people the context needed to act on a media authenticity signal.

A detector produces analysis. A product has to decide what happens next. The space between those two responsibilities is where a review workflow earns its value.

Start by identifying the decision: whether to publish a clip, request another recording, escalate a suspicious submission, or simply inform an analyst. Each action needs a different level of evidence and a different path for uncertainty.

Use more than two product states

An application may want a binary action, but its analysis can have several meaningful outcomes. Distinguish at least:

• Analysis still in progress.
• A completed result that can be evaluated under your policy.
• A result that requires further review.
• An input that could not be assessed.

These are suggested workflow states, not DeepFAIC API enum values. Map them deliberately from the documented lifecycle, lane status, coverage, and prediction fields.

Give reviewers the original context

Present the submitted media alongside analysis. Include the submission's source context when it is available and appropriate for the reviewer to access. For video, make it easy to inspect the sections associated with available chunk or frame scores.

Keep audio and visual analysis visible when both apply. A reviewer should be able to see a disagreement between lanes or a failed lane instead of discovering it only in a raw API response.

Use language that reflects the evidence. “Flagged for review under our configured policy” is more informative than an unexplained badge claiming certainty.

Preserve the reason for an action

Record the job ID, relevant results, scoring source, and the reviewer's decision. Keep machine analysis separate from the review outcome so the two can be compared later.

A reviewer may clear a flagged item after checking its context, or escalate a submission that received a low signal for reasons outside the detector's scope. Recording both preserves an honest account of the decision.

Design the uncertain path first

Decide what happens when processing fails, coverage is insufficient, or a reviewer cannot establish the context. Depending on your product, that might mean asking for a new input, requesting an additional verification step, or escalating to a specialist.

Give users a clear next step where your workflow allows it. An inconclusive result should not strand a submission in an unexplained state.

Feed outcomes back into evaluation

Aggregate review outcomes to identify patterns: input conditions that generate unnecessary review, cases your policy misses, and the workload created by a scoring change.

Use these observations to build a representative evaluation set and assess candidate configurations. Review outcomes are useful evidence, but they should also carry the limitations of the review process that produced them.

A dependable authenticity workflow combines a detection signal, a clear policy, and an accountable decision. The interface should make each of those parts understandable.

Read the DeepFAIC documentation to connect this workflow to the deployment and API that suit your application.