A deepfake detection API can help flag synthetic or manipulated media, but it cannot certify that every file is genuine or fake. Before integrating one, decide which manipulations matter, test candidate services on representative uploads, and use scores to route cases—not as automatic proof for consequential decisions.
What does “deepfake detection” need to detect?
The label can cover several different forensic tasks. An API that detects faces altered by a face swap may not answer whether an entire image was generated by AI, whether a video was manipulated outside the face region, or where an edit occurred. Provenance—information about a file’s origin and history—is another distinct question.
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Define the target before comparing services. NIST’s Open Media Forensics Challenge separates manipulation detection and localization from deepfake detection, and distinguishes image from video deepfake tasks. NIST’s Guardians of Forensic Evidence also lists questions such as image authenticity, face-swap identity verification, manipulation localization, source verification, and provenance reconstruction.
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- Face manipulation: Has a face been swapped or otherwise altered?
- Other edits: Does the task include changes outside faces, or locating the manipulated region?
- Origin and provenance: Can the file’s source or editing history be established?
These are not interchangeable answers. State which ones your policy requires, whether you need image and video coverage, and what the system should do when a file has no detectable face or falls outside the service’s documented inputs.
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How do I shortlist image and video detection APIs?
Public documentation describes different product shapes, not a common performance test. The table summarizes documented capabilities; it does not rank accuracy or establish that one service performs better on your media.
| Service | Documented scope and integration | Documented output or deployment | Pricing information |
|---|---|---|---|
| Hive | Separate AI-generated-media and face-manipulation classification for image and video through a detection endpoint. Listed image formats are GIF, JPG, PNG, and WEBP; listed video formats are MP4, WEBM, AVI, MKV, WMV, and MOV. Documentation | Per-face bounding boxes, a yes_deepfake or no_deepfake class, and a confidence score are described in the API reference. |
The model page displayed $6 per 1,000 image requests and $6 per 1,000 video frames when accessed 2026-10-07. Model page |
| Sensity | REST API documentation describes image, video, or audio submission by direct upload or public URL, with Face Manipulation, AI-Generated Content Detection, Voice Analysis, and File Analysis as applicable. API documentation | Returns an overall suspicious or valid verdict, confidence scores, visual explanations from machine-learning services, and file-analysis findings. Cloud and on-premise deployment are described. |
Not stated in the cited public API documentation. |
| Reality Defender RealAPI | The product page describes image, audio, and video analysis via REST or SDKs for Python, TypeScript, Go, Rust, or Java. Product page | Structured manipulation-probability scores and explainable indicators are described. Deployment options beyond the stated API and SDK routes are not established on the cited page. | The page displayed free and business plans, with $399 shown for the business plan while annual billing was selected, plus custom enterprise pricing, when accessed 2026-10-07. The billing interval for the $399 amount was not fully clear on the captured page; verify terms directly before budgeting. |
Compare candidates against the requirements that affect your system: manipulation scope, file and duration limits, frame sampling, score detail, explanations, latency and throughput, pricing unit, deployment and data handling, and performance on your held-out evaluation set. Hive documents separate classification heads; the Sensity and Reality Defender descriptions emphasize different services and integration options. That documentation is useful for shortlisting, but it is not an independent head-to-head benchmark.
Rank #2
Can an API detect a deepfake video?
It can return signals about a video under the model’s defined task, but video results may be based on evaluating frames rather than establishing an unbroken, definitive judgment about the whole recording. The API’s sampling method and your aggregation rule therefore matter: a short altered segment can be missed if it is not sampled, while a single unusual frame can produce a flag that needs context.
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Hive’s documentation gives starting thresholds for its own models: 0.9 on any frame for AI-generated video; for deepfake video, 0.5 on two consecutive frames or on 5% of all frames. These are vendor recommendations to refine for a use case, not universal cutoffs for other APIs or proof that a clip is fake. See the Hive image and video detection guidance.
Before launch, establish and document how the service samples frames, how it handles a video with no faces, and how your platform aggregates frame-level output into an item-level decision. Test different video lengths and altered segments, and measure both missed detections and flags on unmanipulated clips.
How accurate is deepfake detection?
There is no single accuracy number that applies to every model, task, file type, and platform. A confidence score is a model output, not a universal probability that media is authentic or fake. A threshold that is useful for sending an item to a human review queue may be inappropriate for rejecting an account, taking fraud action, or making a public accusation.
Robustness can change when media differs from evaluation examples. NIST’s Guardians program describes assessing generalization across older and newer manipulation techniques and transformed media such as Gaussian blur and video compression; it is an evaluation effort, not a completed ranking of commercial APIs. NIST’s page on GenAI: Deepfakes 2026 reports 45–50% performance degradation in the transition from academic evaluation to operational deployment, attributing that figure to a referenced study. Treat it as a statement reported on that page, not as an expected drop for every product.
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NIST’s OpenMFC leaderboard displayed a video deepfake detection submission with AUC 0.817059, updated 2024-06-13. That is a historical result for one submission on a particular evaluation—not a current claim about a commercial API, nor a direct comparison of the services above.
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How do I evaluate candidates before production?
- Write the decision policy. Specify the manipulation classes you need to detect, which media types are in scope, and which outcomes may follow a flag. Separate routing for review from enforcement decisions.
- Build a representative, labeled test set. Include genuine media, known manipulated examples, current generators relevant to your users, varied compression and resizing, lighting and face sizes, multiple people, and non-face synthetic content if it is in scope. Keep your labels and selected thresholds separate from vendor defaults.
- Test by meaningful slices. Report false positives and false negatives by manipulation type and relevant media characteristics, rather than relying on one aggregate score. Include transformed files such as compressed or blurred media, reflecting the robustness questions identified by NIST.
- Measure the operating path. Test representative file lengths, throughput, latency, frame sampling, retries, asynchronous status handling, and behavior for unsupported or no-face files. Confirm formats, size and duration limits, and current API behavior in each vendor’s documentation and plan.
- Choose thresholds using consequences. Evaluate candidate cutoffs against your own held-out examples and the cost of errors. Keep a human review route and an appeal path for consequential outcomes.
- Recheck after changes. Version the model/configuration and your evaluation set, and evaluate again when the service or your input distribution changes. Ask vendors how model updates and changes are communicated.
How should image and video checks fit into the platform?
Treat detection as one signal in a defined moderation or trust-and-safety workflow. Preserve the submitted media and the relevant model result according to your own retention policy; record enough context to support review and audit, without treating a score alone as a finding of intent or authenticity.
- Low-impact routing: A score or localized face result can prioritize an item for human review.
- High-impact action: Require policy-specific corroboration and a documented review process before rejection, account action, or public claims.
- Uncertain or unsupported result: Define a fallback such as manual review or a neutral status, rather than silently treating an error or missing face as a positive or negative detection.
- Appeals and audit: Keep a path for affected users to challenge consequential decisions and for reviewers to record the basis of an outcome.
For video, calculate unit economics from the platform’s actual media mix and sampling policy. Hive’s listed video price is per frame, not per clip: the displayed $6 per 1,000 frames and $6 per 1,000 image requests were accessed 2026-10-07. A clip’s frame count and sampling approach determine how a per-frame rate translates into usage cost; recheck the model page before budgeting.
What must procurement confirm?
Public product pages do not establish all terms needed for a production decision. Confirm these directly with each vendor before sending user media or relying on results:
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- Data residency, security controls, and any required certifications;
- Service levels, rate limits, support, and contract terms;
- Model versioning, update notices, and whether results remain reproducible after a model change;
- For Sensity, account approval and API-key provisioning: its documentation says new cloud accounts require manual approval before API key creation.
Make the selection only after the vendors have been evaluated using the same labeled media, versioned configurations, transformations, and decision thresholds. The public documentation above does not provide a common current benchmark from which to name a best-performing API.
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