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AI image detection

All-in-one AI image detector

Upload one image and review C2PA credentials, Google SynthID, Meta watermark signals, generic AI fingerprints, and basic metadata in one workflow.

C2PA
Browser-side read
Watermark
SynthID / Meta
Fingerprint
Generic model
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JPG, PNG, WebP, or AVIF up to 10 MB

Upload runs browser-side C2PA first. Full server analysis calls Gemini, a Meta adapter, and Replicate only when those API keys are configured.

Summary
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Detection channels

C2PA Content Credentials

Reads embedded content credentials and manifest data.

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  • Upload an image to run full server analysis.

OpenAI / Google / Adobe provenance

Looks for trusted signer, claim generator, and AI provenance evidence.

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  • Server check required

Google SynthID

Checks for Google AI invisible watermark signals through Gemini/SynthID capability.

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  • Server check required

Meta Stable Signature / Seal

Checks Meta/Stable Signature style watermark signals through an optional adapter.

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  • Server check required

Generic AI fingerprint

Calls a generic AI image detector model as a secondary signal.

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  • Server check required

Basic metadata signals

Records format, size, dimensions, and cache hash evidence.

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  • Server check required

Results are an aggregation of signals, not a legal or factual final verdict. Compression, screenshots, reposting, and edits can remove watermarks or metadata.

How the detector works

The workflow starts with the strongest deterministic signals before using heavier model-based checks. It is designed for reviewers who need a fast first pass, not a final legal ruling.

Last updated: May 20, 2026

1. Provenance

Read C2PA first

The browser reads embedded Content Credentials before upload. A valid credential can identify the signer, claim generator, edit history, and whether an AI tool was declared in the media record.

2. Watermark

Check provider marks

Server analysis can call Gemini/SynthID and a Meta signature adapter when configured. These checks look for provider-specific marks that may survive ordinary viewing but can still be damaged by edits.

3. Fingerprint

Use a model fallback

A generic detector adds a probabilistic signal when no watermark or credential is found. It is useful for triage, but it should never override stronger provenance or watermark evidence.

Methodology and signal hierarchy

AI image detection is most reliable when the tool separates cryptographic provenance, watermark signals, model-based guesses, and ordinary metadata. This page uses that hierarchy so a missing signal does not get mistaken for proof that an image is human-made.

High confidence

A valid C2PA credential or a confirmed provider watermark is treated as the strongest signal because it comes from media provenance or a provider-side marking system.

Medium confidence

A trusted provider name, claim generator, or AI-generation assertion inside a manifest is useful, but it still needs context about validation state and signer trust.

Low confidence

Generic AI image classifiers are fallbacks. They can spot visual patterns, but they are vulnerable to compression, style transfer, screenshots, and domain mismatch.

No verified signal

No signal means no supported evidence was found. It does not prove the image is authentic, human-made, or unedited.

What each detection channel means

The tool reports every channel separately because different AI image generators leave different traces. A single image may contain a signed credential, an invisible watermark, both, or neither.

C2PA Content Credentials

Checks for signed provenance data embedded in the file. When present, it can describe origin, edits, ingredients, and claim generator information.

Trusted provenance

Looks for trusted signers or recognizable tool names from OpenAI, Google, Adobe, and related provenance ecosystems.

Google SynthID

Checks whether a Google AI watermark signal can be detected through configured provider capability. A negative result does not rule out other AI systems.

Meta Stable Signature / Seal

Provides an adapter slot for Meta-style and Stable Signature-style watermark detection when a compatible detector service is available.

Generic AI fingerprint

Uses a model-based AI image classifier as a supplemental signal. This channel is best used for triage, moderation queues, and manual review prioritization.

Basic metadata

Records file type, size, dimensions, and server-side hash. Metadata is helpful context, but it is easy to remove or rewrite.

Limits you should expect

AI image detection is a signal aggregation problem. Screenshots, cropped images, social media recompression, format conversion, and manual retouching can remove metadata or weaken invisible watermarks.

Model-based detectors also have false positives and false negatives. A polished illustration, 3D render, stock photo, or heavily edited camera image can resemble generated media even when it is not.

Use high-confidence signals to support review decisions, and keep inconclusive results in a human review workflow when the image has legal, brand safety, editorial, or moderation consequences.

Privacy and upload handling

Browser-side C2PA detection runs before server analysis. The full server check sends the image only after the user chooses to run it, and provider calls are only made when the relevant API keys are configured.

The upload limit is intentionally set to 10 MB even though Cloudflare plan limits can be higher. This keeps memory usage predictable and reduces risk when the Worker has to inspect binary image data.

The API uses a SHA-256 hash for cache lookup so repeated checks of the same image can reuse the prior result without rerunning every provider.

AI image detection FAQ

Practical limits and privacy details for image verification.