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No single website or detector can prove that an image or video is authentic. The dependable approach is to preserve the best available file, check its provenance, trace where it appeared, inspect metadata and visual or temporal details, and corroborate the depicted event independently. A result should be reported as evidence for or against a claim—not as an automatic authenticity verdict.
First, identify what you are actually trying to prove
“AI-generated,” “manipulated,” and “misleadingly presented” describe different problems:
- AI-generated: the pixels, frames, voice, or other elements were produced by a generative model.
- Manipulated: an existing recording was altered, perhaps by compositing, retouching, speed changes, dubbing, or conventional editing. AI may or may not have been used.
- Misleadingly presented: a genuine recording is paired with a false date, location, caption, speaker, or event. The file can be completely real and still support a false claim.
An AI-assisted edit also does not, by itself, prove that the event shown is false. Your conclusion should answer two separate questions: How was this media made or changed? and Does it support the claim attached to it?
Use this evidence-first workflow
Work from the strongest evidence to the weakest. Keep notes on the file name, download time, account that supplied it, URLs or messages where it appeared, and every check you performed. Those records matter if the post is deleted or the issue becomes consequential.
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- Preserve the best copy. Download the original file when you can. Keep the untouched copy read-only and make working copies for frame grabs or metadata inspection. A screenshot, screen recording, messaging-app recompression, or platform transcode can remove metadata and introduce visual artifacts. A metadata-free copy is therefore neither proof of fakery nor proof of authenticity.
- Check supported provenance signals. Use the provider’s own checker when the suspected generator and file type are supported. OpenAI’s public checker looks for supported OpenAI signals such as SynthID or a trusted C2PA manifest. Gemini can check supported SynthID and Content Credentials for supported media. Availability and supported formats can change, so record the tool name and date of the result.
- Interpret a positive result narrowly. A detected OpenAI signal indicates provenance associated with supported OpenAI technology; it does not establish that the image is unedited, that the depicted event happened, that the caption is correct, or that anyone owns the material. Google describes Content Credentials as origin and history information and explicitly says: “Content Credentials don’t tell you if an image, video, or audio file is definitely AI-generated.” A watermark can answer “where did a supported signal come from?” without answering “is this claim true?”
- Treat a missing result as inconclusive. OpenAI lists unsupported products or models, export paths, file types, and stripped metadata as reasons a signal may not be found. Google notes that repeated alterations can eventually make SynthID undetectable. Do not translate “no signal detected” into “camera original.”
- Trace earlier appearances. Run the clearest frame or image through a reverse-image service and search distinctive captions, names, landmarks, or phrases. Open earlier results, compare upload dates, and read the surrounding context. A recording that predates the alleged event can disprove the claimed timing or location. Reverse search alone usually cannot establish whether the original file was generated, edited, or authentic.
- Inspect the original file’s metadata. EXIF may contain a camera model, capture time, lens, exposure settings, GPS, thumbnail, or software history. Video containers may expose codec, creation-time, frame-rate, and editing fields. Check whether the claimed device and time are plausible, and whether a software tag conflicts with the story. Metadata is easy to remove and can be rewritten, so treat it as a clue and preserve the raw output rather than editing it.
- Examine visual and temporal continuity. Look for clues, then test them against other evidence. Useful leads include garbled lettering, malformed hands or jewelry, repeated textures, inconsistent shadows or reflections, impossible light direction, faces that change between frames, edge halos around inserted subjects, and audio that does not match mouth movement. Compression, ordinary retouching, frame interpolation, or a low-resolution upload can create similar defects. A single oddity is not proof.
- Corroborate the event independently. Search for contemporaneous reporting, official records, weather or astronomical conditions, geolocated photographs, and unrelated footage from the same place and time. Compare landmarks, shadows, signage, accents, and chronology. For a video, establish whether the camera movement, speech, reflections, and background activity remain continuous across frames.
- Use automated detectors only as additional evidence. A classifier score is meaningful only within the tool’s documented coverage and validation. NIST recommends evaluating media-analysis systems against both manipulated and genuine examples to estimate false positives and false negatives, with manual review. Its deepfake evaluation work identifies generalization to newer generators and robustness after post-processing as continuing challenges. Avoid describing an unvalidated score as a universal probability. OpenAI’s guidance is direct: “No detection method is foolproof.”
- Escalate high-consequence cases. If the material could affect someone’s safety, reputation, money, employment, elections, health, or a legal decision, preserve the original and all provenance records, seek independent corroboration, and consult a qualified forensic examiner. Do not download, repost, or circulate suspected intimate or harmful deepfakes while investigating them.
What each check can and cannot tell you
| Check | Best evidence supplied | Coverage or limitation | Common failure mode |
|---|---|---|---|
| Provider provenance checker | Whether a supported provider signal or manifest is present | Usually limited to that provider’s signals, models, formats, and export paths | Unsupported, stripped, or degraded signals produce no result |
| Content Credentials or C2PA history | Recorded origin and editing history when a valid credential travels with the file | Only describes recorded actions; it is not a universal authenticity certificate | Credentials can be absent, removed, or incomplete |
| Reverse-image or frame search | Earlier copies, similar media, likely source, and timing | Search indexes vary and may not find private or newly posted material | Finds reuse without proving how the earliest copy was created |
| EXIF and container metadata | Possible device, capture, software, and timing clues | Original file is needed; fields can be missing or altered | Rewritten metadata creates false confidence |
| Visual and temporal review | Inconsistencies in text, anatomy, lighting, reflections, motion, or lip-sync | Needs a sufficiently clear file and frame-by-frame comparison | Compression or ordinary editing resembles generative artifacts |
| Automated detector | A model-specific classification signal | Performance depends on generator, media type, version, and post-processing | False positives, false negatives, and poor generalization |
| Independent corroboration | Whether the depicted event, date, place, and participants fit outside evidence | May be difficult when an event is private or very recent | Confirms context without necessarily identifying the production method |
How to check provenance with provider tools
OpenAI-generated images
For an image suspected to come from an OpenAI system, submit the best available file to OpenAI’s public verification tool. A positive supported signal is evidence of an association with OpenAI provenance technology. It does not certify that the image has never been edited, that a person or scene is depicted accurately, or that the post’s caption is truthful. Record whether the result identifies SynthID, a trusted C2PA manifest, or another supported signal.
If the tool finds nothing, check whether the file is a screenshot, a re-export, a converted format, or a product or model outside the tool’s coverage. Those conditions can remove or prevent detection.
Gemini and supported Google signals
Gemini can check supported SynthID and Content Credentials for supported media. A credential may show origin and subsequent editing history when that information remains attached. It is still a provenance record, not a statement that the media is definitely AI-generated or that its accompanying claim is accurate. Save the result and the exact file submitted because a later repost may no longer carry the same information.
Why provider checks should not be combined into one “AI score”
A positive result from one provider does not test every other generator. Conversely, a negative result in one checker cannot rule out another provider, an unmarked model, or a file whose signal was stripped. Treat each output as a scoped observation and continue through source, metadata, visual, and contextual checks.
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Reverse-search the image or a representative video frame
Use the highest-resolution still you can obtain. Crop out borders, app controls, and subtitles, but retain an uncropped copy for comparison. Search several distinctive regions when the full image returns nothing: a face, logo, landmark, sign, or unusual background object.
- Record the earliest credible result, not merely the first result in a ranking.
- Compare the wording, crop, resolution, and edits between versions.
- Check whether the alleged location, weather, clothing, architecture, or event schedule fits the earliest date.
- For video, repeat the process with frames showing different details; one frame may match a genuine source while another reveals a later edit.
An older upload can establish that a caption’s date or location is wrong. It cannot, by itself, prove whether the earliest upload was generated or manipulated.
Read metadata without over-trusting it
Metadata is strongest when you possess a direct camera original and a coherent chain of custody. Look for:
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- capture time, time zone, and sequence numbers consistent with the event;
- lens, shutter, aperture, ISO, GPS, and orientation values that fit the scene;
- software or export tags showing an editor, messaging app, or image converter;
- video codec, frame rate, duration, audio tracks, and creation fields that fit the recording.
Do not infer that a missing camera model means “AI.” Social platforms routinely strip fields, and anyone with basic software can rewrite them. Keep a cryptographic hash or an untouched archival copy if the file may be examined later.
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Visual clues for images
Text and symbols
Zoom into signs, labels, screens, and book spines. Generators may produce nearly readable lettering, inconsistent character spacing, or symbols that change shape. Verify suspicious text against a real sign, language, font, or product rather than relying on appearance alone.
Anatomy, objects, and repetition
Inspect hands, ears, teeth, jewelry, eyeglasses, cables, and object junctions. Count repeated people, windows, leaves, or patterns. Generative systems can merge boundaries or duplicate textures, but severe compression and aggressive sharpening can create comparable artifacts.
Light, reflections, and geometry
Trace the direction and softness of shadows, highlights on skin and metal, reflections in glass or water, and perspective lines. An inconsistency is a lead to test against the scene’s light sources and camera position, not a final verdict.
Temporal and audio clues for video
Watch at normal speed, then step through frames around suspected edits. Check whether facial identity, hair, earrings, teeth, and background edges remain stable. Compare lip movements with consonants and breaths, and listen for abrupt changes in room tone, reverberation, or microphone quality. Look for cuts hidden by camera motion, repeated frames, unnatural motion blur, and reflections that fail to track the subject.
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These signs can also result from dubbing, stabilization, frame-rate conversion, ordinary splicing, or a poor upload. Corroborate with an independent recording or a reliable account of the event before labeling the video a deepfake.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret detector scores
Detectors are classifiers trained on particular examples. Their output can change when a generator is updated, when a file is resized or recompressed, or when an editor adds subtitles and music. A score such as “80% AI” is not automatically an 80% probability that the media is synthetic; that interpretation requires a documented validation study matching the file type, population, generator mix, and operating conditions.
Ask four questions before using a score:
- What media types, generators, languages, and versions were evaluated?
- Were genuine examples tested alongside manipulated ones?
- How did resizing, screenshots, transcoding, cropping, or added audio affect error rates?
- Is the result independently reproducible, and has a human reviewer examined the file?
NIST’s evaluation guidance uses measures such as ROC curves and area under the curve to characterize systems; it does not provide a universal consumer-detector accuracy percentage.
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When the evidence remains uncertain
Use calibrated language. “A supported provenance signal links this file to a provider” is stronger and narrower than “this proves the video is fake.” “The earliest copy we found predates the claimed event” addresses timing, not production method. “No supported signal was found” means only that the particular check returned no signal under its coverage.
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If checks conflict, preserve all versions and document the conflict. A signed history may outweigh a visual hunch for provenance, while independent eyewitness footage may outweigh a provenance record for what actually happened. When neither side is decisive, the correct conclusion is unresolved—not authentic and not fake.
A practical escalation checklist
- Original file preserved, with working copies clearly separated.
- Source account, message, download time, and file hashes recorded.
- Provider checks run and their scope and date noted.
- Reverse-search results captured with dates and surrounding context.
- Metadata exported without altering the source file.
- Specific visual or temporal anomalies documented by frame or timestamp.
- Independent sources checked for the event, place, people, and timing.
- Potential harms considered before sharing the material.
- Qualified forensic review obtained when a decision could affect safety, reputation, money, or law.
Conclusion
Checking a suspected deepfake is a chain-of-evidence task, not a hunt for a magic detector. Start with provenance and the original file, use reverse search and metadata to establish history, inspect image or video continuity for leads, and verify the underlying event independently. Provider signals and automated scores can add useful evidence, but both have defined coverage and failure modes. When the available evidence cannot distinguish among generation, editing, and misleading context, say so plainly and avoid amplifying the claim.
Frequently Asked Questions
Does a Content Credentials check prove that a file is real?
No. It records origin and editing information when a supported credential is present. It does not certify that the depicted event happened, that the caption is accurate, or that the media is definitely AI-generated.
If no watermark or detector signal is found, is the media authentic?
No. The generator may be unsupported, the file may have been re-exported or stripped of metadata, or later alterations may have degraded the signal. A missing result is inconclusive.
Can reverse-image search prove that an image is AI-generated?
No. It can reveal earlier copies, reuse, and contradictions in timing or context. It generally cannot identify how the earliest copy was produced.
When should I involve a forensic examiner?
Use qualified independent review when the material could affect safety, reputation, finances, employment, elections, health, or a legal decision, especially if provider checks and contextual evidence conflict.
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