How to Detect AI-Generated Content — and Why No Single Detector Is Sufficient

“Is this image AI-generated?” — the question sounds simple, but it is not. There is no method that can reliably answer it for every piece of content. What does exist are several independent approaches, each with very different strengths. Understanding these makes it clear why a thorough review always consists of multiple layers.

1. Cryptographic Provenance (C2PA)

The strongest form of evidence does not come from analysing the image, but from the image itself. The C2PA standard — supported by Adobe, Microsoft, Google, OpenAI and several camera manufacturers — embeds a cryptographically signed provenance statement directly in the file. This records which tool created or edited the content.

The major advantage: this is not a probability, but a verifiable statement. The drawback: metadata does not always survive every processing step. If an image is recompressed, passed through a CMS, or shared via social networks, the signature often disappears. Missing credentials are therefore not proof of human authorship — they simply mean there is no information.

2. Metadata and Embedded Traces

Alongside C2PA, there are simpler but surprisingly rich sources. The IPTC standard includes a field for the “digital source type”, which can explicitly mark a file as algorithmically generated — this very marker is referenced in the EU code of conduct for labelling. EXIF and XMP fields often reveal the tool used to create the content. And with PNG files from popular image generators, the full prompt and settings are stored in the file header — a clear-cut piece of evidence.

3. Invisible Watermarks

Some providers embed a pattern into the pixels that is invisible to humans. The advantage over metadata: it can survive compression and cropping. The catch: such watermarks can usually only be read by the party who created them. In practice, this means you are reliant on the cooperation of the providers.

4. Statistical Classifiers

If all other traces are missing, the content itself can be analysed. Modern classifiers achieve high accuracy rates for images and can often even identify the generating model. For texts, the situation is more delicate: good detectors are often correct, but every one of them produces false positives and false negatives. Carefully written human texts can be wrongly classified as machine-generated, while edited AI texts can slip through undetected.

A detector provides a probability, not a truth. Mistaking this for a fact is to confuse statistics with evidence.

Why Layers Alone Are Not Enough

Combining these methods makes the result significantly more robust: a cryptographic proof outweighs any estimate, an embedded prompt is hard evidence, and a classifier covers cases where all traces have been removed. Nevertheless, some uncertainty always remains — and this is not a weakness of the technology, but a feature of the problem itself.

That is why at Provifai we take a deliberately different approach: our assessment provides the evidence, but the final statement is made by the operator. For each finding, you see which method triggered and how reliable it is — and you confirm or reject it. This protects against the worst consequence of automated detection: that someone is accused of content they actually created themselves.

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