Methodology
How Image Analyze evaluates an image
Every check runs the full workflow. Results are translated into plain-language findings that map back to underlying evidence, so the user can see what signals were found and why they matter.
1. AI-generation analysis
The system checks for pixel-level indicators commonly associated with AI-generated imagery. This helps identify images that may have been produced by generators such as Midjourney, DALL-E, Stable Diffusion, or similar tools.
2. Metadata and EXIF review
The system inspects available camera and file metadata, including device information, timestamps, software traces, and missing-data patterns. A photo with intact camera metadata means something different from a file with all metadata stripped or overwritten.
3. Pixel forensics
The system reviews compression artifacts, error-level patterns, noise behavior, and other structural inconsistencies that can point to editing, synthesis, or unusual processing. These are supporting indicators, not standalone proof.
4. Reverse image search and context
Reverse search helps determine whether the same or similar image has appeared elsewhere online. Matches can provide important context, such as prior publication, reposting, reuse, or association with AI galleries and other sources.
How findings are presented
The product summarizes signals as findings and likelihood indicators. It is intentionally designed to avoid false certainty. If the evidence is mixed, the result should stay mixed.
Limits of the methodology
Modern AI-generated images can evade detection. Real photos can lose metadata during sharing. Edited images are not necessarily deceptive. Reverse image search coverage is incomplete. The best use of this workflow is to reduce uncertainty, not eliminate it.