How Do I Run a Deepfake Detection Proof of Concept?

How do you run a deepfake detection proof of concept?

Run it as a decision experiment with rules fixed in advance. Before any vendor sees data, write a one-page charter: the decision the detector will inform (approve an onboarding, hold a payment, escalate a video), the channels and media types in scope, the current baseline, the metrics that define success, the result that stops the test, and who signs the final decision. Freeze those rules. Changing a gate after an unfavourable result turns a proof of concept into a sales exercise.

Then stage the exposure. Start with offline replay on labelled historical or commissioned media. Move to shadow mode, where the detector scores live traffic but nobody acts on its output. Add advisory mode, where reviewers see the score alongside their normal evidence. Only then grant limited production authority, capped by volume or value, if the earlier stages met their gates. Most enterprise POCs cover the first two or three stages in four to eight weeks; the length depends far more on data access and security review than on the detector itself.

What data should the test set contain?

The genuine media matters as much as the fakes. False positives are measured on authentic submissions, so take real images, selfies or calls from your own channel, with the same devices, compression and lighting your customers produce. A detector that looks perfect on clean studio portraits may flag a large share of low-light phone selfies.

For attacks, cover the techniques you actually face: face swaps, fully generated faces, AI photo edits, replayed screens, virtual camera injection and, if documents are in scope, tampered identity documents. Include recent generators, because a test set built only from 2019-era datasets such as FaceForensics++ measures yesterday's threat. Pass a portion of samples through the same resizing and compression your pipeline applies. Keep a sealed holdout that no vendor sees until the final scoring run, and stop pilot media from leaking into later tests.

How should developers implement a proof of concept?

Developers should integrate the detector behind a thin adapter in the backend, call it in shadow mode first, and log every raw response with its model version before any business logic reads it. The adapter lets you swap or run vendors in parallel against identical inputs. Logging the raw response, not just a pass or fail, lets you recompute results at different thresholds after the fact.

Handle the boring parts early: timeouts and retries with idempotency keys, a defined behaviour when the service is unavailable, payload size and format limits, and synchronous calls for images versus asynchronous jobs for longer video or audio. Record end-to-end latency from your own servers, not only the vendor's processing time, because network distance and file upload often dominate. Store media and results according to the retention terms agreed in the security review, and delete test data when the POC closes.

With NeuroVerify, the integration surface is a REST API plus SDKs for Android and Kotlin, iOS and Swift, and Flutter on mobile, and Python, TypeScript and Node.js, Go and Java on the backend, with an MCP server for AI agents. Neural Defend describes integration as two lines of code and teams as live in under 24 hours; budget the rest of the POC time for test design, review and sign-off.

Which metrics should define POC success?

POC success should be defined by error rates at the operating point you will actually run, plus the operational cost of those errors, not by headline accuracy. On imbalanced sets accuracy can look high while a detector misses most attacks or flags most genuine users. The VendorBench-100 authors make the same warning and rank systems by Matthews correlation coefficient (MCC), which stays informative when classes are uneven.

Set each gate from your own loss model. If a missed deepfake in onboarding costs far more than a manual review, you can accept more false positives; in a high-volume consumer flow, the reverse may hold.

How do you compare vendors fairly inside one POC?

Give every candidate the same sealed corpus, the same file versions, the same operating-point rules and the same load test, and score them with your own code. Let vendors tune thresholds only on a separate calibration split. Report raw counts and confidence intervals, because on a few hundred attack samples the difference between two detectors may be noise.

Public benchmarks are a useful sanity check before you invest in a POC. On the public VendorBench-100 benchmark, 36 systems were tested on one adversarial set of 100 still images: Neural Defend ranked first with an MCC of 0.876, Reality Defender second at 0.646 and Gemini third at 0.389. One VendorBench author also co-authored Neural Defend's ARCAS 1B paper, so treat the result as a public reference you can rerun rather than a verdict; the corpus and results are released for that purpose. The benchmark does not cover video, audio, injection attacks or latency, which your POC still needs to test.

What a proof of concept cannot tell you

A POC samples a few weeks of traffic. It will under-represent rare attack types, peak load, slow drift in customer devices and the behaviour of attackers who adapt once they learn a detector is in place. Parallel tests can also be biased if candidates receive different cases or more reviewer attention.

Close the gap with a written acceptance record that authorises a bounded use, a post-launch review date, re-test triggers such as a new model version or channel, and a rollback plan. NIST's AI Risk Management Framework and ISO/IEC TS 25058:2024 both frame evaluation as tied to a named context, which is the right posture: acceptance covers the use you tested, not every use.

Running a proof of concept with Neural Defend

Neural Defend POCs typically pair two products. NeuroVerify scores face images, video or audio in one call across six signals, including Face Manipulation Detection, AI Generative Model Analysis and Live Presence Verification, and returns one risk score with signal-level evidence; latency is under 200 ms at p95 on eligible image requests under defined test conditions, with methodology on request. NeuroDetect gives your analysts a case workspace to examine disputed samples with heatmaps, frame and audio-segment views, and a forensic report that keeps model and version context.

Deployment for the POC can match production: managed cloud API, private cloud or VPC, secure container, on-premises or air-gapped, scoped during evaluation. The underlying ARCAS 1B model's pooled accuracy, reported in the ARCAS paper, ranges from 97.65% on OpenFake to 99.99% on GenImage. Those are research benchmarks; your sealed test set decides whether the model fits your channel.