A deepfake score is a signal. Here’s how to read it.
Read scores alongside thresholds, coverage, and model status to build a more useful media authenticity review.
It is tempting to compress media analysis into a single number and a red or green badge. The number is convenient, but a useful decision needs context: what was analyzed, how much of it was covered, and whether the analysis completed successfully.
DeepFAIC's full Edge job record includes an aggregated prediction and applicable audio, video, or image lanes. Each lane can report a score, threshold, coverage, status, and errors. Those fields belong together in your application.
Start with status
A missing score is not a low score. A failed analysis is not a clean result. Before displaying a verdict, check the job lifecycle and the status of the applicable lanes.
The Edge response distinguishes queued, processing, complete, and error. Its final prediction_label can be FAKE, REAL, or NONE; NONE represents a case where a verdict could not be produced, such as when every model failed.
Build an explicit “unable to assess” state into your interface. Otherwise, a default value or an empty response can silently become a misleading green badge.
Read the threshold beside the score
A score has meaning within the scoring configuration that produced it. The threshold describes the decision boundary used for that lane. Store and display both when an analyst needs to understand the result.
Do not relabel a raw score as a percentage probability unless you have established that interpretation for the model and your evaluation data. A value of 0.8 should not automatically become “80% certain this is fake.”
Use a short, concrete presentation instead:
Video analysis completed. Score: 0.62. Applied threshold: 0.40. Review recommended under the configured policy.
This example is illustrative; it is not a measured result or a universal threshold recommendation.
Coverage changes the question
Coverage helps describe the extent of analysis. Read its meaning in the endpoint documentation and evaluate what it means for your media. A successful lane with limited coverage deserves different treatment from a fully analyzed sample.
Low coverage might lead your workflow to request a better input, inspect a different part of the recording, or route the item to review. Choose that policy deliberately. Do not silently treat unassessed material as verified.
Keep audio and video visible
A video submission can carry both visual and audio signals. The full record exposes those lanes separately. If they disagree, preserve that disagreement for the reviewer instead of displaying only a convenient average.
Chunk scores and available frame scores can help a reviewer locate parts of a recording to inspect. Treat them as analysis outputs to investigate, with the original media and its context available alongside them.
Record how the decision was produced
Edge includes prediction_source, identifying the default configuration or the selected scoring profile. Save it with the job ID, result, and review outcome. This gives your team a way to compare results after a scoring configuration changes.
A good review interface answers four questions: did analysis finish, what signal did it produce, what material did it cover, and what policy turned that signal into the next action?
For an example response and field definitions, see the Edge API documentation.