7 Ways to Detect AI-Generated Images (2026)

There's no magic button that tells you "this image is AI-generated" with 100% certainty. Anyone who claims otherwise is selling something. But by stacking multiple detection methods — each with different strengths and blind spots — you can get a pretty reliable picture.

PixPipe's detector runs seven checks simultaneously. Here's what each one actually does, what it's good at catching, and exactly where it falls apart.

EXIF metadata: the fingerprints your camera leaves behind

Every real camera — including your phone — stamps photos with EXIF metadata. Camera model, lens, shutter speed, ISO, GPS coordinates. It's the photographic equivalent of a fingerprint.

AI-generated images either have zero EXIF data (suspicious in itself) or carry synthetic metadata that doesn't quite add up. An image claiming to be from an iPhone 15 but missing the specific fields Apple always includes? That's a red flag.

The weakness: this is trivially easy to fake. Someone can inject plausible EXIF data into an AI image with free tools. And stripping EXIF from a real photo (something our own tool does for privacy) makes a legitimate photo look suspicious by this metric. It's useful as one signal among many, but never conclusive on its own.

SynthID: Google's invisible signature

This is the most definitive check we have — but only for one generator. Google embeds an invisible statistical pattern called SynthID into every Gemini image. It survives cropping, resizing, and compression. If our detector finds it, the image came from Gemini. Full stop.

The obvious limitation: Midjourney, DALL-E, Stable Diffusion, and every other generator don't use SynthID. A clean SynthID check doesn't mean "not AI" — it means "not Gemini."

C2PA: the digital paper trail

C2PA is an industry standard for recording an image's creation history — think of it like a chain-of-custody document embedded in the file itself. Adobe Firefly uses it. Some versions of DALL-E use it. A handful of camera manufacturers are starting to adopt it too.

When present, it's very informative. You can see exactly what tool created the image and what edits were made afterward.

The catch: it's metadata, not pixels. Strip the metadata and the C2PA record is gone. Also, adoption is still spotty — most generators don't support it yet. It's a great signal when it's there, but its absence doesn't tell you much.

Resolution patterns: AI generators have habits

This one's subtle. AI generators don't output random resolutions — they have defaults. Midjourney v6 loves 1024×1024 and 1536×1024. DALL-E 3 outputs exactly 1024×1024, 1024×1792, or 1792×1024. Stable Diffusion XL defaults to 1024×1024.

So if an image is precisely 1024×1024 with no cropping artifacts, that's a soft signal. Not proof — plenty of real photos get cropped to perfect squares — but it adds to the overall picture.

This breaks down completely once someone resizes the image. A Midjourney output scaled to 2000×2000 for Etsy won't trigger this check at all.

Filename patterns: laziness is detectable

People are lazy about renaming files. Midjourney exports with a specific hash format. DALL-E literally puts "DALL-E" in the filename. Stable Diffusion UIs often include the seed number.

This is probably the weakest signal — anyone who renames the file defeats it — but it's also the fastest check and catches more images than you'd expect. Most people just download and upload without thinking about the filename.

Visual artifacts: the uncanny details

AI images have tells. Hands with too many fingers (or too few). Text that almost reads correctly but not quite. Reflections that don't match the scene. Texture patterns that repeat in ways real surfaces don't.

Our detector looks for these statistical patterns — things like inconsistent lighting direction, impossible geometry, and characteristic noise profiles that differ from camera sensors.

The problem: the latest generators are getting really good. GPT-4o and Midjourney v6 produce images with minimal artifacts. And on the flip side, heavily compressed or edited real photos can trigger false positives. This method works best as a tiebreaker when other signals are ambiguous.

Frequency analysis: what the math sees

This is the most technical check. Real photographs and AI images have different characteristics in the frequency domain — essentially, how pixel values change across the image at different scales. AI generators tend to produce subtly different patterns in high-frequency detail than real camera sensors.

It's a probabilistic signal, not a yes/no answer. Think of it as "this image's frequency profile is more consistent with AI generation than with camera capture, with X% confidence."

Heavy post-processing can throw this off in both directions. A real photo run through aggressive sharpening might start looking AI-ish to frequency analysis. An AI image that's been compressed and re-saved multiple times might wash out the telltale patterns.

Why combining them matters

Any single method is easily defeated or produces false positives. The power is in the combination.

EXIF analysis misses images with faked metadata — but frequency analysis catches them. SynthID only works for Gemini — but filename patterns catch Midjourney exports. Visual artifact detection misses high-quality generations — but C2PA catches them if the metadata is intact.

When three or four methods agree, the confidence goes way up. When they disagree, that's informative too — it usually means the image has been significantly post-processed, which is worth knowing regardless of whether it's AI-generated.

No detection system is perfect. But seven imperfect methods working together are a lot better than one.

You can try all seven on any image at PixPipe's AI detector — runs entirely in your browser.