The Tool Desk
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What counts as a deepfake?
The word “deepfake” is an umbrella term for media that has been generated or manipulated using AI. It can refer to images, audio, video, or combinations of media. It does not tell you which technique was used, whether the entire file is synthetic, or whether the content is deceptive.
Some generative systems create new material from a description. In its 2025 pilot evaluation plan, NIST describes an image-generation task with the objective of generating realistic images from textual descriptions, as well as an image-discrimination task that classifies whether an image was AI-generated: NIST’s 2025 NIST GenAI (Pilot) Evaluation Plan for Image Generators.
Other workflows manipulate existing media. NIST’s forensic program identifies face swaps, body swaps, and changes to context as relevant categories for constructing challenging deepfake tests. A clip might therefore contain genuine footage that has been altered, or real footage presented in a misleading setting; “deepfake” does not necessarily mean every pixel or sound was generated from scratch.
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Why can deepfakes be hard to detect?
Generation and manipulation methods change
Detection systems are built and evaluated against particular examples and methods. New generators or different ways of manipulating media can produce material that does not resemble the examples a tool was trained or tested on. A result is meaningful only in light of what kinds of media and manipulations the tool has actually been evaluated against.
Real-world files are not clean test samples
Media is often edited, compressed, copied, or passed through social platforms before someone investigates it. These processes can erase, distort, or introduce visual and audio artifacts. NIST’s Guardians of Forensic Evidence program calls for representative evaluation data and discusses “dirty,” post-processed evidence, including low-bitrate surveillance video and media with social-media compression artifacts. A detector that works on pristine files may not behave the same way on a downloaded, resized, or repeatedly shared clip.
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Test performance may not transfer to operational use
NIST’s GenAI: Deepfakes 2026 program page states that current detection systems show “performance degradation of 45-50% when transitioning from academic evaluation to operational deployment.” This is the figure stated on that program page, not a universal accuracy score or a finding that every detector fails by that amount. The page summary does not provide enough methodological detail to generalize the figure to a particular tool or file.
Visual oddities are clues, not proof
Unnatural lighting, strange details, or imperfect audio-video synchronization may prompt closer scrutiny, but none establishes that a file is fake. Genuine recordings can also contain artifacts, while generated or manipulated media may lack obvious flaws. A visual inspection is one observation, not a reliable verdict.
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How should you assess a suspicious image or video?
Start with where the file came from and how it reached you. NIST’s Examining Digital Media guide recommends asking whether provenance has been verified and whether the media was edited, enhanced, copied, compressed, or stored. For a consequential claim, consider these steps:
- Look for the earliest available version. Seek the original file or a copy from the person or organization that recorded or published it, rather than relying only on a repost or screen recording.
- Trace the file’s handling. Note who supplied it and whether it was edited, enhanced, copied, compressed, or stored in a way that could affect the evidence.
- Check context independently. Look for corroboration from other credible sources, such as separate recordings, contemporaneous reporting, or statements from people directly involved. A matching repost is not necessarily independent confirmation.
- Use automated detection cautiously. Treat a detector result as one piece of evidence. Ask what media types, manipulation methods, generators, and post-processing conditions were represented in its evaluation, and whether the result includes information about uncertainty and errors.
- Match the tool to the question. A tool that estimates whether an image is synthetic does not automatically establish who appears in it, whether a particular region was edited, or whether the surrounding context is truthful.
When a decision could materially affect someone, avoid treating a detector score or a visual tell as conclusive. Preserve the file and its available provenance, seek independent corroboration, and involve qualified reviewers where appropriate.
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How do detection, provenance, and watermarking differ?
These approaches answer different questions. NIST’s overview of technical approaches to digital content transparency covers provenance, watermarking, detection, prevention, testing, and auditing as distinct approaches.
| Approach | What it can indicate | What it does not establish by itself |
|---|---|---|
| Automated detection | A model’s assessment of whether content appears synthetic or manipulated, depending on the tool’s task. | That the file is definitively fake, who made it, or whether its claims are true. |
| Provenance or content-authenticity information | Information about a file’s origin or history, when available and verifiable. | That the content is truthful or that every stage of its history is known. |
| Watermarking | A signal associated with content, if a watermark is present and can be detected. | That all generated media is watermarked or that a missing watermark proves a file is authentic. |
A detector’s probability or confidence score, a provenance record, and a cryptographic or content credential are different forms of evidence. They should not be treated as interchangeable guarantees.
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What should organizations require from a deepfake detector?
For organizations evaluating a system, the important question is not just whether it detects selected test examples. NIST’s forensic evaluation program emphasizes representative data, post-processed evidence, and continuing validation as threats and software change. Compare systems on:
- Media and task: Does the tool cover images, video, audio, or combinations, and is it detecting synthetic origin, locating manipulated regions, checking identity, or addressing another question?
- Realistic conditions: Were test files compressed, edited, low-resolution, or created by unfamiliar generators in ways similar to the intended use?
- Both kinds of error: Are false positives and false negatives measured, and are the results relevant to the consequences of a mistaken decision?
- Evidence returned: Does the tool produce a score, identify regions, or report provenance? What does that output actually mean?
- Ongoing validation: How will performance be reassessed as generators, manipulation methods, and software change?
For higher-stakes identity proofing specifically, NIST’s SP 800-63-4 Identity Proofing Requirements says automated algorithms should be tested on both available attack artifacts and genuine media to establish baseline performance and expected false-positive and false-negative rates. It also calls for manual review to augment automated analysis and decision-making. Its discussion of sensor authentication, device attestation, and human-in-the-loop cues applies to identity-proofing and remote collection settings; it is not a universal checklist for judging any online video.
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