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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThere is no reliable visual giveaway that proves an image was made by AI. Odd details can be clues to investigate, but a sound assessment combines close inspection with information about how the work was made and any available provenance records. Treat detector scores and digital credentials as evidence with limits—not as verdicts.
What visual clues can—and cannot—tell you
View the image at full resolution and look for inconsistencies in details, materials, perspective, or repeated forms. Ask whether separate parts of the image agree with one another. An apparent mismatch may justify closer inspection, but it does not establish how the image was made: the available evidence does not support any single visual flaw as a dependable, standalone test.
Visual judgment varies, too. A 2024 study compared automated detectors with three groups of human assessors using human-made art across seven styles and AI images from five generative models. It tested eight approaches: five automated detectors and three human groups—180 crowdworkers, more than 4,000 professional artist volunteers, and 13 expert artists experienced in spotting AI images. The authors reported that Hive and the expert artists performed well, but had different weaknesses: Hive was more vulnerable to adversarial perturbations, while the experts produced more false positives. These results describe that study, not a universal ranking of current detectors or a guarantee about images made with newer systems. Read the study by Ha et al.
That distinction matters when the stakes are high. A suspicious-looking detail can be a reason to ask questions; it is not grounds by itself to accuse a creator.
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How to check an image’s origin
- Inspect the image closely. Note specific inconsistencies rather than relying on a general impression. Treat each one as a question to investigate, not proof.
- Ask about the process. If appropriate, ask who made the work and how. Sketches, layered source files, or work-in-progress images may help explain the process. They can be incomplete or fabricated, so weigh them alongside other evidence.
- Check provenance information. If the file or publishing platform provides a credential or other history, examine what it records, who signed it, and whether it is associated with the image you are viewing.
- Use detector results cautiously. Treat a score as one input, not a finding of authorship. The 2024 study found different weaknesses across its tested methods; a result from one detector cannot establish certainty.
- Keep separate questions separate. Whether AI was involved, whether a work is aesthetically successful, and whether copyright applies are different questions. The answer to one does not settle the others.
How the evidence types differ
| Evidence | What it may establish | What can limit it | What it does not establish by itself |
|---|---|---|---|
| Visual inspection | Details worth examining more closely. | Some human assessors and automated methods make different kinds of errors; a visual anomaly is not a reliable universal test. Ha et al., 2024 | That AI made the image, or who made it. |
| Process materials | Context about how a creator says the work developed. | Materials can be incomplete or fabricated. | Authorship or AI involvement with certainty. |
| C2PA Content Credentials | A cryptographically bound record of assertions about an asset’s origin, modifications, tools, or AI use, when such a credential is available. | The record may be absent; its signer and association with the asset need checking. | That the recorded assertions are true, or the identity of the person who personally created the work. C2PA Explainer, version 2.2 |
| AI detector | A tool’s assessment of whether an image may have been generated by AI. | Performance depends on the method and image; tested approaches have different weaknesses. Ha et al., 2024 | A definitive account of authorship or a sound basis, by itself, for accusing a creator. |
What Content Credentials mean
C2PA Content Credentials can carry assertions about an asset’s origin, changes, tools, and AI use in a cryptographically bound record. The standard’s version 2.2 explainer distinguishes the integrity of the record from the truth of its contents: credentials address whether data is well-formed, free from tampering, valid, trusted through the signer, and associated with the asset. They do not decide whether the assertions themselves are true. See the C2PA explainer.
So a valid credential is useful evidence about a recorded history, not a certificate of artistic merit or a guarantee that a named person personally made the image. Consider who signed it and what it actually says; C2PA describes credentials as complementary to media literacy, fact-checking, and digital forensics.
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An absent credential is not evidence of human authorship. Metadata may be stripped, a file or tool may not be supported, and signals can be degraded. OpenAI’s help article describes these limits for provenance signals associated with content generated or exported by its tools: platforms, editing, or conversion can remove metadata, while transformations can degrade watermarks. A detected signal also does not establish accuracy, legal ownership, context, or creator identity. Those statements concern OpenAI’s supported signals, not every AI system. OpenAI’s explanation of C2PA in ChatGPT images.
AI involvement is not one single category
An image may be wholly generated, created by a person using AI-assisted editing, or assembled from AI output and substantial human work. Evidence of a tool’s involvement does not, by itself, describe how much expressive work a person contributed. If that distinction matters, ask about the process and look at the available history rather than treating “AI” and “human-made” as two simple, mutually exclusive labels.
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What AI involvement means for U.S. copyright
In its January 29, 2025 guidance, the U.S. Copyright Office said copyright may cover AI-assisted or mixed works when a human author contributes sufficient expressive elements. Its analysis includes human-authored material that remains perceptible and human creative arrangements or modifications; merely providing prompts is not enough under the Office’s current position. AI assistance or AI-generated material within a larger human-created work does not automatically prevent copyright protection. Read the Copyright Office announcement.
This is U.S. guidance, not a universal rule. Copyright approaches differ among countries, and whether a work qualifies for copyright is separate from whether it is aesthetically valuable, whether AI involvement was disclosed, or whether training data was lawful. A Library of Congress interview with Copyright Office staff discusses the distinction between AI assisting human expression and AI standing in for human expressive choices, as well as differing approaches internationally. Read the interview.
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