About AI Image Detector
We built AI Image Detector to make image screening more transparent: measurable signals, separate scores, plain-language limitations, and no invented certainty.
Why this tool exists
Synthetic and heavily edited images now move through newsrooms, classrooms, marketplaces, social feeds, and private messages. People need a quick way to organize evidence, but many detector interfaces hide their method behind a single confident label. AI Image Detector takes a different approach. It shows distinct probabilities for AI generation, AI editing, conventional editing, manipulation, deepfake risk, metadata consistency, and authenticity.
The tool is intended for preliminary review. It can help a journalist prioritize verification, a teacher discuss media literacy, a creator inspect a downloaded asset, or a photographer understand what survived an export. It is not designed to accuse a person or certify a file for court.
Our analysis approach
The server validates the submitted encoding and dimensions, parses available EXIF and XMP data, checks camera and software fields, measures file size relative to pixels, samples color and local differences, estimates edge and noise behavior, and searches readable structure for generator and C2PA-related terms. A documented weighted formula combines these signals.
No image is secretly sent to ChatGPT, Claude, Gemini, OpenAI, Anthropic, or another external model. Results come from the server-side heuristic code included in this project. That keeps the method inspectable and prevents the product from claiming an integration it does not have.
Privacy by architecture
Images are submitted only after the Analyze Image button is pressed. The route processes the bytes in memory, returns measurements, and does not write a public result, permanent image record, or shareable image URL. Basic limits restrict file type, size, dimensions, malformed content, and request frequency.
No system can promise absolute security. Users should avoid uploading material they are not authorized to process, and should follow institutional rules for confidential, sensitive, or regulated images.
Our commitment to honest limitations
Final flattened images lose history. Metadata can be removed or forged, recompression can mask earlier edits, and genuine computational photography can resemble synthetic processing. New generators also change faster than static detection rules. We therefore include an Inconclusive verdict and describe false-positive and false-negative risk.
When a decision could affect rights, safety, reputation, employment, or legal outcomes, preserve the original file and seek qualified human forensic analysis. Our product is strongest as a transparent screening and education tool.
Questions about this page
Who owns AI Image Detector reports?
The report is generated for the user who submits the file. The site claims no ownership of uploaded content; users remain responsible for having permission to analyze it.
Do you train an AI model on uploaded images?
No. This implementation neither trains a model nor permanently stores submitted images. It calculates a temporary heuristic report from the uploaded bytes.
Need more context? Return to the AI Image Detector tool, read our image forensics guides, or contact us.