1. Start with the original file
Original files can contain useful evidence: camera model, capture time, exposure values, software names, C2PA provenance, or generation workflow data. Screenshots and reposted images often strip that evidence away.
2. Check metadata and provenance
Camera metadata supports a real-photo interpretation, but it is not proof. Missing metadata is also not proof of AI generation. C2PA or JUMBF provenance records can help when they are present, especially if they declare editing or AI creation.
3. Look for generator traces
Some generated images include text fields such as prompt, negative prompt, seed, sampler, model, workflow, ComfyUI, Automatic1111, Stable Diffusion, Midjourney, or DALL-E. These are strong clues when they appear in the file.
4. Inspect visual details carefully
Hands, text signs, reflections, shadows, repeated textures, warped objects, and background people can reveal generation artifacts. But visual inspection can also be fooled by blur, compression, wide-angle distortion, heavy editing, and low resolution.
5. Compare with the source context
Save the source URL, author, publish date, and surrounding claim. Run reverse image search to find earlier copies. A detection result becomes much more useful when it is tied to where the image came from and what it is being used to prove.