When this workflow helps
An AI-image detector can be one signal in a broader review when checking a suspicious picture or evaluating image provenance. It should not be treated as a definitive verdict about who created an image or whether it depicts a real event.
A step-by-step workflow
Learn to treat AI-image detection as an evidence review: consider model limitations, image context, provenance and why a detector cannot prove authorship. Use the following sequence as a checklist; exact controls and supported formats are shown in the live tool.
Preserve the original file and context
If authenticity matters, use the highest-quality original available and record where it came from. Screenshots, recompression and social-platform processing can change image signals.
Run the available analysis
Use the detector to examine the image and read its explanation or output carefully. Treat the result as a model response based on the patterns it learned, not as independent proof.
Look for corroborating evidence
Check source history, publication context, reverse-image matches where available, creator statements and reputable reporting. Visible artefacts alone are rarely enough to establish authorship.
Communicate uncertainty clearly
If sharing a conclusion, distinguish “the tool flagged this image” from “this image is definitely AI-generated.” Explain the limits and any supporting evidence instead of presenting a probability as a certainty.
Settings, quality and privacy checks
False positives and false negatives are possible. AI-generated images can be edited, compressed or passed through tools that change their characteristics; authentic images can also have synthetic-looking texture. High-stakes claims need independent provenance and review, not a single automated result.
Before using any browser tool with confidential material, check what the specific studio says about local processing, model downloads, external services and supported formats. Do not assume that every app has the same storage or network behaviour simply because it is linked from RuntimeHub.
Common mistakes to avoid
- Presenting a detector output as proof that a person or event is fake.
- Ignoring the source, editing history or compression of the image.
- Applying one threshold to every generator, image type and context.
A useful final habit is to keep the original input until you have opened the output and confirmed that it fits the intended destination. When the result will be submitted, published or used as evidence, inspect it in the actual application that will receive it.
Frequently asked questions
Can an AI image detector prove that an image is fake?
No. It can provide an indication based on learned visual signals, but it cannot establish the full provenance of an image on its own.
What should I do when the result is uncertain?
Preserve the source, seek provenance and independent corroboration, and describe the detector’s result as one piece of evidence rather than a verdict.
Put the workflow into practice
Use PixelTruth to work through the ideas in this guide. Keep the source available, make the changes you need, and check the result before saving or sharing it.
- Start with the file or information you want to work on.
- Use the available controls to shape the result.
- Preview the output before saving or sharing it.