AI Image Detector

Wondering "is this image AI-generated?" This free AI image checker first reads what the file declares about itself — camera data, software tags, C2PA Content Credentials — and then runs a pixel-level Deep Check that still works on a screenshot, where every platform has already stripped the metadata away. Missing metadata is never treated as proof of anything, and an "AI" verdict is re-checked before you see it. Flags Midjourney, DALL-E, Stable Diffusion, Flux and other generators.

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Major AI Tools

Detects DALL-E, Midjourney, Stable Diffusion, Adobe Firefly and more.

Multi-Signal Analysis

Examines metadata, software signatures, compression, and noise patterns.

Free to Check

Metadata analysis is free and unlimited, no signup. The pixel-level Deep Check is free once a day, then costs credits.

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How it works

This AI image checker works in two passes. The first reads the file: real camera photos carry EXIF fields — sensor data, lens model, shutter speed — that AI generators never produce, and generators frequently leave a software tag naming themselves, or a signed C2PA manifest that does. That is positive evidence, and when it is there you get an answer in one second. What the first pass will not do is hold an empty file against you. Instagram, X, Facebook, WhatsApp and Discord strip metadata from every upload, and a screenshot never had any — so "no camera data" is a fact about the pipeline the image travelled through, not about how it was made. Checkers that quietly add points for missing EXIF end up accusing photographers of generating their own photographs. This one says what the file proves, what it does not, and nothing more. Noise, compression and dimensions are printed as context and carry no weight in the verdict. The second pass is the Deep Check, and it is the only pass that can judge an image carrying no markers at all — which, after a trip through any social platform, is most of them. The Deep Check ignores the file header and examines the pixels: diffusion artefacts, impossible or melted geometry, garbled text and signage, wrong finger and limb counts, inconsistent light and shadow, waxy skin, and the missing optical noise (sensor grain, chromatic aberration, lens blur) that every real lens leaves behind. It returns a verdict, a confidence figure, the reasons it saw, and its closest generator match. An "AI-generated" verdict is then put back to the model on independent passes and only survives if they agree — a single sampled answer is not stable enough to accuse someone with, and a false AI verdict on a real photograph is the worst mistake this tool can make.

When to use it

Verify dating profiles and social media accounts where fake profile photos are common. Screen user-submitted images on platforms, forums, and marketplaces. Check photography contest entries for AI-generated submissions. Evaluate images in journalism, legal discovery, and insurance claims where authenticity determines trust. Asking "is this image AI?" — upload it and get a verdict with the evidence behind it. Combine this tool with the AI detection guide for manual visual techniques, or visit the AI photo checker page for more context.

How it compares

Most AI picture detectors return one pixel-model percentage, and many quietly count missing metadata against an image — which is exactly how a real photograph ends up labelled AI. Scanly runs two passes instead. It reads the file's own evidence first — EXIF, software tags, signed C2PA manifests — and never treats stripped metadata as proof of anything. Then a pixel-level Deep Check judges the image itself, so it still answers on a screenshot where the metadata is long gone, and an "AI" verdict is re-checked across independent passes before you see it. Your image is used only for that analysis and auto-deleted afterward; it is never kept or shared. For a broader comparison of tools, see the tool comparison page or the dedicated Scanly vs Jimpl breakdown.

Tips for accurate results

Use original files whenever possible — re-saved or screenshotted images lose metadata that helps with detection. Social media platforms strip EXIF data on upload, so images downloaded from Instagram or Twitter will show fewer signals. If the Deep Check comes back uncertain, cross-check with the Authenticity Checker for editing traces and the Screenshot Scanner to rule out screen captures.

AI image generation has advanced rapidly, and the gap between generated and real photos continues to narrow. Tools like Midjourney v6, DALL-E 3, Stable Diffusion XL, Adobe Firefly, and Flux now produce synthetic images that are difficult to distinguish visually from photographs — including deepfake portraits, AI-generated landscapes, and fake product photos used in scams. However, generated images still leave detectable traces in file structure and metadata. Whether you need to detect Midjourney output, check for DALL-E artifacts, or identify Stable Diffusion images, the metadata layer provides the most reliable first signal for any AI photo checker workflow.

For a complete verification workflow when a single AI image scanner result is inconclusive, combine multiple forensic tools. Start here to check metadata signals, then run the image through the ELA Scanner to look for compression inconsistencies, the FFT Spectrum analyzer to detect periodic GAN artifacts in the frequency domain, and the Noise Analysis tool to check for uniform noise patterns that real cameras never produce. This multi-signal approach is what professional fake image detectors and deepfake photo checkers use — no single method is definitive, but convergent evidence across tools builds a reliable verdict.

For a deeper dive into visual detection techniques that complement metadata forensics, read our guide on how to spot deepfake photos. The Forgery Heatmap combines multiple detection signals into a single visualization, and the photo forensics hub provides the full toolkit for synthetic image detection and photo authentication.

For specific use cases, see our step-by-step guides: Check if an Image Is AI Generated, Detect Midjourney Images, Detect DALL-E Generated Images, and Detect Stable Diffusion Images. Also: Detect Deepfake Photos, Check if an Image Is Real, and AI Art Detector.

Frequently Asked Questions

How accurate is the AI detection?

No AI detection is 100% accurate, and any tool claiming otherwise is selling you something. The metadata signals are certain when they are present (a Midjourney software tag is a Midjourney software tag) but absent on most images. The pixel-level Deep Check reads the image itself and returns a confidence figure with the specific reasons behind it; when the evidence is thin it says "uncertain" rather than guess, because wrongly calling a real photograph AI-generated is the worst error a detector can make.

Can you detect an AI image with no metadata?

Yes — that is what the Deep Check is for. It ignores metadata entirely and examines the pixels: diffusion artefacts, impossible or melted geometry, garbled text and signage, wrong finger and limb counts, physically inconsistent light and shadow, waxy skin, and the absence of real optical noise such as sensor grain and lens blur.

Does it work on a screenshot from Instagram, X or WhatsApp?

Yes. Every platform strips EXIF metadata on upload, and a screenshot never had any to begin with — which is why metadata-only checkers return "no signals found" on exactly the images people most want to check. Run the Deep Check on the screenshot; it reads the pixels, not the file header.

Which AI generators can be detected?

Midjourney, DALL-E, Stable Diffusion, Adobe Firefly, Flux, Leonardo and other popular generators. Where the file still carries metadata, the generator is often named outright in a software tag. Where it does not, the Deep Check judges the pixels and offers its closest generator match alongside the verdict.

Is my image stored?

No. The metadata analysis runs on our server and the upload is auto-deleted within an hour. If you run the Deep Check, a downscaled copy of the image is sent to our AI processing provider for that single request and is not retained by us. Nothing is kept long-term, shared, or used to train anything.

Can AI detection be fooled?

Metadata can be wiped or forged in seconds, so metadata alone is easy to defeat. Pixel-level detection is harder to fool but not impossible: heavy compression, small images, and aggressive re-editing all erase the evidence. Treat the verdict as evidence, not proof, and combine it with the ELA Scanner, FFT Spectrum and Noise Analysis when the answer matters.

How do I check if an image is AI-generated?

Upload it. The checker first reads what the file declares about itself — camera data, software tags, C2PA Content Credentials. If a generator named itself, you have your answer immediately. If the metadata is gone, that is not held against the image: the checker says so and stops, because a stripped file proves nothing. Then run the Deep Check, which analyses the pixels themselves and returns a verdict, a confidence figure, and the specific visible reasons.

Is this a free AI photo checker?

The metadata analysis is free, unlimited, and needs no signup. The pixel-level Deep Check runs a vision model, which costs us money per image, so you get 1 free Deep Check per day and each one after that costs a single credit. No subscription, no watermark on the answer.

What if an AI image has been re-saved or screenshotted?

Re-saving and screenshotting destroy metadata, so the metadata signals go quiet — that is expected, not a clean result. The Deep Check is the answer for these files: it works from the pixels, so a screenshot is a perfectly valid input. Very heavy compression or a very small image will still weaken any verdict.

Can it detect AI-enhanced or partially edited photos?

Partly. The Deep Check is strongest on fully AI-generated images; a real photo with an AI-retouched sky or a removed object may not trigger them. For localised edits, use the ELA Scanner, Clone Detection and Noise Analysis, which look for inconsistencies between regions rather than judging the image as a whole.

Why does it say "no answer" instead of giving me a percentage?

Because a percentage built from missing metadata is a lie with a decimal point. Every social platform strips EXIF, so "no camera data" describes the upload pipeline, not the image — a holiday snap from WhatsApp and a Midjourney render look identical to a metadata reader. Rather than invent a number, the checker tells you what the file does and does not prove, and sends you to the Deep Check, which reads the pixels. When the Deep Check does call an image AI-generated, that verdict is re-run on independent passes and only survives if they agree.

Is this an AI art detector for contest submissions?

It can screen contest entries: run the Deep Check on each submission and read the listed reasons, not just the verdict. For strongest confidence, combine it with FFT Spectrum analysis, which reveals frequency-domain GAN artifacts invisible to the eye. Never disqualify anyone on a single automated verdict.

Complete AI Image Verification Workflow

No single tool can definitively determine whether an image is AI-generated. For the strongest analysis, combine multiple detection methods that examine different aspects of the image.

Start with the AI Detector to read the file's metadata and software tags, then run its pixel-level Deep Check on the image itself. If the result is inconclusive, run the image through Error Level Analysis (ELA) — AI-generated images often show unusually uniform error levels compared to real photographs that have varied compression across different areas.

Next, use the FFT Spectrum Analyzer to examine frequency-domain patterns. GAN-generated images leave distinctive grid artifacts in the frequency spectrum that are invisible to the naked eye but clearly visible in the FFT output.

For suspected face manipulation or deepfakes, the Noise Analysis tool reveals inconsistencies in noise distribution — spliced or AI-generated regions often have different noise characteristics than the surrounding image. Finally, the Authenticity Checker aggregates multiple signals including EXIF consistency, software traces, and compression patterns into an overall assessment.

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