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No single visual tell can confirm that a video is a deepfake. Treat odd-looking details as reasons to investigate, then check the clip’s source, context and any available provenance before deciding what it supports—or sharing it.

What visual clues should you look for?

Inspect the video for details that do not seem to fit together, but treat each one as a possible anomaly rather than proof of manipulation. Face-swapping and other edits can leave visible artifacts; compression, poor recording quality and ordinary editing can create them too.

  • Face edges and skin tone: Look for a visible blend or abrupt transition around the face, or skin that appears inconsistent with the rest of the person or scene.
  • Lighting and shadows: Check whether the face and body appear lit from the same direction as the surroundings, and whether shadows behave consistently.
  • Mouth movement and audio: Watch whether the speaker’s lip movements appear to match the spoken words. A mismatch is worth checking, but does not by itself establish that the video is synthetic.
  • Background and image quality: Notice whether the face, background or other parts of the frame differ strangely in sharpness or resolution, or whether the background behaves inconsistently.

The Associated Press’s explainer on spotting AI-generated imagery discusses visual signs and checking mouth movement against audio in suspected doctored speaking video. It also quotes AI expert Henry Ajder saying that “creative prompting can sometimes eliminate this and many other signs of AI manipulation.” In other words, visual clues can be absent or evaded; their presence is not a verdict either way. Associated Press explainer

How to verify a suspicious video

  1. Pause before sharing. Note the account, date, caption and claimed location. Separate the claim about what happened from the question of whether the footage may have been altered.
  2. Find the earliest available version. Search for the clip or distinctive frames, then identify the original uploader or publisher if possible. Reposts may omit context or information about the file’s history.
  3. Inspect visual details cautiously. Check face boundaries, skin-tone transitions, lighting and shadows, lip movement against audio, and consistency in the background. Do not treat an artifact—or the absence of one—as a conclusive test.
  4. Check the caption against independent context. Look for credible reporting or other footage of the event. Test whether the date, location, speaker and claim fit what can be corroborated. A sensational or implausible caption calls for extra checking, not an automatic fake verdict.
  5. Check for Content Credentials, if available. Use a C2PA-aware viewer to inspect the credential’s integrity and recorded provenance. Read who signed it and what origin or editing assertions it contains. Treat the record as information about the asset’s history, not as proof that the depicted event happened as claimed.
  6. Interpret detector results carefully. A score is not a binary fact unless you understand the tool’s evaluation data and error rates. NIST’s requirements for covered identity-proofing providers call for testing relevant systems on genuine and manipulated media, documenting expected false-positive and false-negative rates, and augmenting automated decisions with manual review. These are requirements for that setting, not a universal consumer checklist. NIST SP 800-63A
  7. Escalate high-stakes cases. If a decision could have serious consequences, seek qualified expert review. Preserve the original file if it is lawfully available and record where it came from. NIST SP 800-63A addresses identity-proofing providers; it is not a consumer forensic certification procedure.

What different kinds of evidence can establish

Evidence What it can support What it cannot establish by itself
Visual inspection Possible anomalies such as face-edge blending, inconsistent lighting or lip-sync problems. Whether a clip is authentic; artifacts can have other causes, and visual signs may be evaded. AP
Source and context checks Who published a clip, whether an earlier version exists, and whether independent sources support or contradict its caption. AP Whether the pixels are untouched; corroboration of an event is not itself a technical authentication of a particular file.
Content Credentials and provenance Recorded information about an asset’s origin and edits, and whether a credential remains associated with an unchanged asset. C2PA specifications Whether the content is true, accurate or factual. C2PA says provenance alone cannot establish those things; credentials are also optional, so their absence is not evidence of fakery. C2PA specifications
Automated forensic analysis A tool’s assessment, if its performance has been evaluated on relevant genuine and manipulated media and its error rates are understood. A guaranteed correct verdict. NIST’s evaluation and manual-review guidance applies to covered identity-proofing systems, not every detector marketed to consumers. NIST SP 800-63A

How to interpret Content Credentials

Content Credentials are optional provenance information. When present, they can record an asset’s origin and edits and provide a way to check whether the credential remains associated with an unchanged asset. Read the recorded assertions and signer rather than treating the presence of a credential as a general authenticity seal. A valid credential supports what is recorded within its trust model; it does not verify the truth of the scene or the caption. No credential may simply mean provenance information was not attached. C2PA specifications

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Why a detector score is not a final answer

Automated media analysis can make errors. NIST SP 800-63A says, for its identity-proofing context, that “Algorithmic analysis of media and automated decisioning SHOULD be augmented by manual reviews to address detection errors.” It also gives “high latency, synchronization issues, inconsistent skin tone and resolution” as examples for trained proofing agents in remote identity-proofing settings—not as a validated checklist for ordinary viewers. NIST SP 800-63A

For a consumer-facing detector, look for information about what media it was tested on and its false-positive and false-negative rates. Without that context, its output should be treated as one piece of evidence, not a decision about what happened.

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