You usually cannot determine whether a scientific microscope image or video was AI-generated from its appearance alone. The strongest assessment combines the original acquisition files and lab records, the image’s processing history, provenance credentials or supported watermarks, and the relevant journal’s disclosure and methods requirements. A missing credential or a detector’s “real” result does not prove authenticity.
Start by preserving the exact file
Keep the image or video as you received it, and note where and when you obtained it. Record whether it was downloaded, screen-captured, recompressed, or exported. Preserve the highest-resolution version available. For a video, retain the original clip and any available frame sequence rather than relying on a social-platform preview: reposting or taking a screenshot can remove file-based provenance information.
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Request the source data and acquisition records
For a research image, ask for the original acquisition file, microscope or camera details, relevant instrument or lab records, and the processing history. For a video, request the source sequence, frame rate, and processing pipeline. Compare the published crop or rendered image with the original and, where relevant, other fields of view or the underlying data. Preserve the files and comparisons you review.
These records can help establish how the media relates to the research workflow, but they still require expert interpretation. As the HHS Office of Research Integrity (ORI) explains, provenance can record origin and modifications; it cannot decide whether an edit was scientifically appropriate.
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Check credentials and watermarks—and understand their limits
Content Credentials
Content Credentials based on the C2PA standard can provide a cryptographically signed, tamper-evident record of a file’s origin and recorded edits, including information such as who made changes, when, and with what software or device. ORI identifies the C2PA Viewer as a free, open-source option for inspecting credentials. A credential documents recorded history; it does not establish why an edit was made or whether it was proper for a scientific image.
SynthID
A positive SynthID verification can indicate a signal from a generation system covered by that watermark. The result is limited to participating, supported systems; no signal does not rule out other models or workflows. Use the provider’s verification system for supported content and record the tool and exact result.
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Credentials may be absent because the capture or editing tools did not support them, or because metadata was stripped along the way. ORI cautions that the absence of credentials or a watermark is not proof that content is authentic. A negative check means only that the particular tool found no signal within its scope.
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Treat visual clues and detector scores as leads, not verdicts
Visual inspection may flag suspicious structures or inconsistencies, but no reviewed source establishes a reliable visual tell or universal detector for every microscopy modality and video workflow. In a microscopy-focused study, Zecca and coauthors reported a survey of 101 professionals: participants recognized authentic and wholly AI-generated images well, while recognition of hybrid images could improve. The reported finding is not an accuracy rate for all readers, tools, or scientific images.
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NIST’s OpenMFC framework separates image and video deepfake detection from manipulation-detection tasks. That distinction matters: a detector addresses a defined forensic question, not every question about how scientific data were generated. Do not treat a score as the probability an image is fabricated unless the specific tool has been independently validated for the relevant modality, transformations, and population. The sources cited here provide no generalizable accuracy figures or microscopy/video detector comparison.
Read the disclosure and the journal’s actual policy
Rules differ between publishers and between uses of AI. AI-generated content, AI-assisted analysis, and conventional image processing are not interchangeable categories. Check the target journal’s current instructions and read the Methods section, captions, and any disclosure statements alongside the media.
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CDC guidance
The CDC’s May 28, 2026 guidance, Considerations for Disclosing Generative AI Use in Scientific Work, recommends that content produced in part or whole by generative AI be clearly disclosed in an accessible manner. It suggests identifying the affected content, action taken, AI tool, purpose, and human oversight. For analytic or methodological uses, it recommends enough detail about prompts, settings, inputs, and validation to support reproducibility, subject to security requirements.
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Elsevier policy
Elsevier’s generative AI policy for journals says generative AI must not create or alter primary research images, including microscopy images. It distinguishes these from some explanatory images and data visualizations: AI-supported visualizations must be directly derived from underlying data using reproducible methods, and AI-assisted research methods should be described reproducibly. Elsevier also calls for relevant tool and model details and disclosures in captions or Methods sections. These are Elsevier rules, not a universal legal standard.
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Compare what each check can establish
| Method | Useful evidence | Main limitation |
|---|---|---|
| Original acquisition files and lab records | Workflow history and comparison with submitted media | Access is required, and records need expert interpretation. |
| C2PA Content Credentials | Signed origin and edit history when the workflow created and retained credentials | Adoption is incomplete; metadata can be stripped; credentials do not judge scientific appropriateness. |
| SynthID verification | A watermark signal from supported participating generation systems | No signal does not exclude unsupported tools or workflows; verification is provider-specific. |
| Visual review or a general detector | Potential anomalies to investigate further | There is no universal conclusion or established microscopy/video-wide performance rate. |
| Journal disclosure and methods review | Declared tool use, purpose, version, oversight, and reproducibility details | Publisher rules vary, and disclosure alone does not independently prove the described process was followed. |
These distinctions are consistent with guidance from ORI, Zecca and coauthors, the CDC, Elsevier, and NIST’s Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency, published November 20, 2024. NIST’s report covers broad approaches to provenance, watermarking, detection, testing, and auditing; it is not a validation study of microscopy detectors.
Escalate a concern carefully
If an important discrepancy remains, document the file’s source, the checks performed, comparisons with primary records, and the specific unresolved issue. Contact the authors or journal through their established process and give them an opportunity to clarify. A visual impression or detector result alone is not a sound basis for a public allegation of misconduct. ORI presents provenance as support for expert review, not a replacement for it.
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