Published 2026-10-12 00:00:00 +0000 UTC · Updated 2026-11-05

How to Tell if an Image Is AI-Generated

When an unfamiliar or surprising image appears online, the first question is often whether it was made with AI. Dozens of websites promise a quick percentage score, yet real photographs are routinely flagged as synthetic while AI-generated pictures pass as human-made.

A reliable check separates verifiable evidence inside the file from guesswork. You can inspect both signed provenance and unsigned metadata locally in your browser with the AI evidence & document metadata checker and the image metadata & AI marker viewer.

1. Check signed C2PA Content Credentials

The strongest form of disclosure is a cryptographically signed C2PA Content Credential. When an image generator, camera or editing app attaches a Content Credential, it embeds a tamper-evident manifest describing how the file was created or modified:

• trainedAlgorithmicMedia declares that the media was generated using a trained AI model.
• compositeWithTrainedAlgorithmicMedia declares that AI-generated content was combined with another image (such as generative fill or object replacement).
• algorithmicallyEnhanced indicates non-generative algorithmic adjustments (such as denoising or sharpening) and does not mean the picture was generated by AI.

Read how to check whether an image discloses AI use for how cryptographic binding and signer trust work.

2. Inspect unsigned EXIF, XMP and PNG text markers

Many AI tools do not attach a full C2PA manifest, or their files pass through workflows that only write standard image metadata. Inspecting the image container with the image metadata viewer can reveal:

• IPTC DigitalSourceType in XMP: The IPTC Photo Metadata Standard defines Iptc4xmpExt:DigitalSourceType values using the same cv.iptc.org/newscodes/digitalsourcetype/ vocabulary as C2PA.
• Software and generator tags: EXIF Software, Artist, XMP CreatorTool and PNG text chunks often record names such as Adobe Firefly, Midjourney, DALL·E, ChatGPT or Imagen.
• Stable Diffusion and ComfyUI generation parameters: Local diffusion tools commonly store the exact prompt, seed, sampler, model hash and node graph inside PNG tEXt or iTXt chunks (parameters, prompt, workflow) or JPEG UserComment.

Unlike C2PA manifests, unsigned EXIF, XMP and PNG text chunks are not cryptographically bound to the pixels—anyone can edit or remove them. Treat them as useful disclosures rather than tamper-proof proof.

3. Why missing metadata never proves “human-made”

Most social media platforms, messaging apps and CMS upload pipelines strip EXIF, XMP and Content Credentials when resizing or re-encoding images. Taking a screenshot of an AI image also creates a brand-new file with your device’s screenshot metadata and none of the original’s AI labels. See how photo metadata removal works.

Because metadata is so easily lost:

• Positive disclosure is informative: Finding a valid C2PA AI declaration or a ComfyUI prompt tells you how the file was produced.
• Absence is inconclusive: An image with no credentials and no EXIF could be a stripped camera photo, a screenshot, or an unlabelled AI generation.

4. Why “AI percentage” detectors are unreliable

Visual AI classifiers attempt to guess from pixel statistics, noise patterns or JPEG compression artefacts. In practice:

• Re-compressing, resizing or applying a phone filter alters the high-frequency pixel statistics those classifiers rely on.
• Studio lighting, computational smartphone photography (HDR, night mode, portrait blur) and heavy retouching trigger false positives on real photos.
• A percentage such as “78% AI” sounds scientific without showing a single piece of verifiable evidence.

File Sniff deliberately avoids guessing from pixels. Instead, combine local file inspection (check Content Credentials and view EXIF/XMP metadata) with provenance tracing: locate the earliest published copy, ask for the original camera or export file, and verify the context independently.