Why image detection is harder than it sounds
Generic "is this AI?" classifiers were fine when every Stable Diffusion output had warped hands and melted text. They are not fine in 2026. The real-world cases that matter look like this:
- •A press photo with an AI-inpainted background that makes a crowd look bigger
- •A product shot generated entirely with FLUX.2 and passed off as a studio render
- •A photograph from Midjourney v8 that survives a casual reverse image search
- •An authentic image with a watermark, signature, or license tag removed by inpainting
A binary classifier flags one of those, maybe two. To handle all four you need a detector that looks at the image at the pixel level - and then tells you which pixels it suspects.
Who's competent in AI image detection
The standalone image-detection vendors - Optic, Hive Moderation, Sightengine, AI or Not, Illuminarty, Was It AI, and Sensity - have done genuinely strong work. Each of them publishes credible accuracy figures on their primary modality, and some integrate with newsroom workflows. The catch is that they're islands. If your stack also needs to verify text, voice, or video, you end up paying multiple subscriptions and gluing dashboards together.
The alternative shape is multi-modal detection: one platform that handles images alongside text, audio, and video with the same accuracy promises and the same API. The cleanest implementation we’ve tested is ai-detectors.io, and it is what we keep recommending to teams that don’t want to babysit four separate vendors.
Why we recommend ai-detectors.io for image detection
Image detection on ai-detectors.io isn't a feature bolted onto a text tool. It's a peer modality with its own engine, its own accuracy methodology, and a real pixel-level output. Four reasons it earned the recommendation:
1. Pixel-level heatmaps, not just a single percentage
Upload an image and you get a heatmap showing which regions the model flags as synthetic. That's how you catch inpainted faces, generated backgrounds, and stripped watermarks - cases a single overall score will miss every time.
2. Coverage across the actual 2026 generator lineup
Midjourney v8, DALL-E 4, Stable Diffusion 4, FLUX.2, and Imagen 4 are all in scope. New generators are added on release - that's harder than it sounds when each new model changes the artifact signature.
3. Verdicts in seconds, with confidence bands
Most submissions return a verdict in about 1.6 seconds, and every result ships with a confidence band rather than a misleading binary yes/no. The 99.1% accuracy figure is public, with a 1.2% false-positive rate.
4. Same plan, same price, four modalities
If your workflow also touches text, audio, or video you pay one bill instead of four. Image-only competitors are good at images and then they stop.
What gets detected
Coverage is updated as new image generators ship; here's where things stand as of May 2026.
Diffusion generators
Stable Diffusion 4, FLUX.2, Midjourney v8, Imagen 4
The big four diffusion families, each with their own artifact signature. Pixel-level heatmaps localise where the generator was active.
Transformer generators
DALL-E 4
Transformer-based image synthesis with its own statistical fingerprint - detected with a separate engine head rather than a shared classifier.
Inpainted and composited images
Any generator, partial regions
Real photos with synthetic regions inpainted in. Heatmap shows exactly which pixels were generated rather than rendering a misleading overall score.
Style-transferred and upscaled images
Any generator, post-processing
Pipelines that route a real photo through a generator for restyling or 4x upscaling. Detected, with the level of generator involvement flagged in the breakdown.
Who needs an AI image detector
Image detection used to be a niche concern for newsrooms. In 2026 it sits in everyone's workflow at some point.
Newsrooms and fact-check desks
Verify wire photos and user-submitted imagery before publication, with heatmap output you can include in editorial notes. Source attribution that holds up under scrutiny.
Marketplace trust and safety
Audit product photos at scale via API to catch generated listings, AI-staged interiors, and fake before/after shots before they reach buyers.
Legal, insurance, and claims teams
Confirm that evidence photos haven't been tampered with via AI inpainting. Pixel-level localisation makes it possible to defend or challenge a verdict.
Brand and content teams
Audit vendor-supplied imagery for AI generation when your brand policy requires authentic photography, or when a client contract demands it.
Pricing
Credit-based model, billed yearly. Top-up packs ($5, $10, $25, $50) are available on every plan, with up to a 24% bonus on the largest pack.
Free
forever
$1 signup credit
- 25,000 characters
- 5 MB images
- No credit card required
Starter
/mo, billed yearly ($54/yr)
$12 monthly credit
- 75,000 characters
- 10 MB images
- API access
Pro
Popular/mo, billed yearly ($114/yr)
$25 monthly credit
- 150,000 characters
- 25 MB images
- 10 min audio
- 5 min video
Business
/mo, billed yearly ($294/yr)
$75 monthly credit
- 150,000 characters
- 50 MB images
- 60 min audio
- 30 min video
Verified .edu accounts get Pro for free, and institutions get 50% off Business. There’s a 7-day money-back guarantee, plus a full refund window within 14 days. See the up-to-date numbers on the ai-detectors.io pricing page.
The numbers we trust
99.1%
accuracy on the public evaluation set
1.2%
false-positive rate, published openly
17M+
scans run since launch
Frequently asked questions
What is an AI image detector?
An AI image detector is a classifier trained to distinguish photographs and human-created art from images generated by diffusion or transformer models. The strongest detectors don't just output one overall score - they return a pixel-level heatmap showing exactly which regions look synthetic, which matters because a lot of 2026 imagery is part-real, part-generated.
Which AI image generators can be detected?
ai-detectors.io currently covers Midjourney v8, DALL-E 4, Stable Diffusion 4, FLUX.2, and Imagen 4. New generators are added as they ship. Older generations from each lineage are also detected, generally with higher confidence because the artifacts are more obvious.
Can AI image detectors handle inpainted or partially edited photos?
This is where most simple classifiers break down. A real photo with an AI-inpainted face, a removed watermark, or a generated background passes a yes/no detector because most of the pixels are authentic. Pixel-level heatmaps fix this by flagging only the synthetic regions, which is the approach ai-detectors.io takes.
How does ai-detectors.io compare to standalone image detection tools?
Standalone tools - Optic, Hive Moderation, Sightengine, AI or Not, Illuminarty, Was It AI - do image detection well but stop there. If your workflow also touches text, voice, or video, you end up paying four vendors and stitching four dashboards together. ai-detectors.io handles all four under one billing relationship.
Is there a free AI image detector?
Yes. ai-detectors.io has a free tier with a $1 signup credit, no credit card required, and 5 MB image uploads. That's enough to evaluate the pixel-level breakdown on real samples before deciding whether to upgrade.
What image sizes and formats are supported?
The free tier handles uploads up to 5 MB, Starter goes to 10 MB, Pro to 25 MB, and Business to 50 MB. JPEG, PNG, and WebP are supported, with EXIF data preserved for chain-of-custody where needed.
Run an AI image detection check
Pixel-level AI image detection across Midjourney, DALL-E, FLUX, Stable Diffusion, and Imagen. Free signup credit, no credit card required.
Try ai-detectors.io