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Before publishing a video that may be AI-generated or manipulated, verify what it claims, where it came from, and how it could be understood in context. Provenance records, visual inspection, and detection tools can each provide useful evidence, but none alone establishes that a video is true. Use a repeatable review, document unresolved uncertainty, and describe the media accurately to your audience.
What does a video review need to establish?
Separate three questions that are easy to conflate: how the video was made or changed, whether it shows signs of manipulation, and whether its claims are accurate and presented in context. Provenance can help answer the first; forensic indicators and detectors may inform the second; independent evidence and editorial judgment are essential for the third.
Start with the material claims in the clip, the people or events it depicts, the intended publication context, and the likely harm if viewers misunderstand it. A synthetic-looking image is not itself proof that the underlying claim is false, and realistic-looking footage is not proof that it is authentic.
Use a repeatable pre-publication workflow
- Identify what needs checking. Write down the video’s material factual claims and identify any depicted people, locations, dates, or events that matter. Consider whether the clip could mislead or cause harm if its origin or context is unclear.
- Preserve and trace the source. Keep the original file when available, along with relevant source material. Record who supplied it and any known edits, exports, or transformations. Avoid relying only on a repost or screen recording if an earlier-generation file can be obtained.
- Inspect available provenance. Check for Content Credentials or other provenance signals and record what they report, including the scope of the information. Treat a credential as evidence about an asset’s recorded origin or history—not as independent verification of the events or claims depicted.
- Verify claims independently. Compare relevant assertions with independent, authoritative evidence. Check dates, locations, identities, and surrounding context separately where they affect the story. Do not treat an AI-generated clip as the sole evidence for its own claims.
- Use detection tools cautiously. If you use a detector, record the tool and result, and assess whether its supported media and test conditions fit the file you have. Treat its output as an investigative lead, not a verdict. Do not present a score as a universal probability unless the tool’s calibration and evaluation conditions justify that interpretation.
- Make a publication and disclosure decision. Describe the media in terms supported by your checks, and check the rules and platform policies that apply to your jurisdiction and publication context. If material uncertainty remains, explain it, seek further review, or do not publish.
- Keep an audit trail. Record the source file, checks performed, results, unresolved uncertainties, the editor’s decision, and any disclosure made to the audience.
What Content Credentials can—and cannot—show
C2PA Content Credentials are designed to record provenance in a tamper-evident structure. The Coalition for Content Provenance and Authenticity describes the standard as infrastructure that complements media literacy, fact-checking, and digital forensics, including deepfake detection. Read the C2PA and Content Credentials Explainer, version 2.2 for the standard’s explanation.
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A credential can support a claim about an asset’s recorded origin or history. It does not establish that the recorded assertions are true, that the video’s factual claims are accurate, or that the clip is being shown in the right context. OpenAI likewise cautions that provenance signals do not guarantee that content is accurate, unedited, legally owned, or presented in the correct context (OpenAI, “Provenance signals in OpenAI-generated content”).
Credentials are optional, so their absence alone is not evidence of deception. A file may lack credentials for reasons unrelated to whether it is genuine or misleading. Conversely, the presence of credentials does not remove the need to check the video’s assertions independently.
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How to assess AI-video detectors
Automated analysis can produce false alarms or miss manipulation; its output depends on the tool, media type, and conditions under which it was evaluated. NIST’s forged-media analysis and manual-review provisions appear in SP 800-63A-4, a standard for remote identity proofing. It is a useful example of rigorous review, not a universal editorial requirement: NIST says automated analysis should be tested against forged and genuine media and augmented by manual review to address detection errors.
When comparing available methods, assess what question each answers, which media and transformations it supports, whether another reviewer can inspect its evidence, and what limitations or tested error rates are documented. Avoid ranking tools without current, comparable evaluation data. A detector result can guide further inquiry, but it cannot verify the video’s claims or replace an editor’s assessment.
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Check context and disclosure before publication
Even when the origin or production history is reasonably clear, context can change what a clip communicates. Check whether its date, location, identity claims, and surrounding events are supported by evidence outside the clip. Make clear to viewers when the media is AI-generated or manipulated if that is material to understanding it.
Disclosure obligations depend on jurisdiction, content, and platform. The European Commission’s Code of Practice page describes labeling provisions for deepfakes and AI-generated or manipulated content. Check the current rules and platform policies that apply to your specific publication rather than assuming one standard covers every audience or service.
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