AI Image Detector: 5 Tools Tested Against Midjourney, DALL-E & Flux in 2026

We did not expect the AI image detector landscape to be this uneven. Going into this benchmark we assumed the five public detectors would cluster tightly around the same accuracy. They do not. An ai image detector built for moderation at scale behaves very differently from a free web checker built to answer "is this image AI" for a curious visitor, and the gap between the best and the most permissive is wider than the gap we found on the audio side.

We ran a 40-image corpus through every one of them. The corpus was split evenly across Midjourney v7, DALL-E 3, and Flux 1.1, with a handful of Stable Diffusion and real photographs mixed in as controls. We also ran the same images through Undetectr first, then re-tested, to see whether any detector caught the cleaned versions.

The data is below. The verdict, in advance: every detector here is good at catching raw AI output. None of them caught the cleaned files. If you are a creator trying to publish somewhere that runs a classifier, the question is not which detector is "best." It is whether your image still carries the signature they all share.

What an AI image detector actually does

Every detector in this list is a classifier trained on a corpus of AI-generated images. Training teaches the model to recognise a signature: a constellation of frequency-domain patterns, upsampling artifacts, and statistical regularities that generative models leave behind and that camera sensors do not produce.

The signature is consistent across most casual handling. It survives JPEG re-compression, resizing, colour grading, and light retouching. It does not survive a tool built specifically to scrub it. This is the asymmetry that defines the 2026 ecosystem: detection is reliable, removal is rare.

The detector returns a confidence score, typically between 0 and 1. A platform sets a threshold and flags anything above the line. Public thresholds for image moderation cluster higher than the audio side we benchmarked previously; stock libraries and ad platforms tend to flag around 0.80 to 0.85, which is why lightly processed AI art still gets caught.

If your goal is the opposite of detection, run the image through Undetectr before any of these tools sees it. We explain why below.

At-a-glance comparison

Scores are the average confidence value returned across our 40-image corpus on raw, unedited output. The final row shows what those same detectors returned after the images were processed through Undetectr: under 0.20 on every one, well below any flag threshold.

The 5 detectors, ranked

1. Hive Moderation, the production-grade entrant

Hive is the closest thing in this list to what a large platform actually runs. Its AI-generated-image classifier is part of a broader moderation suite that also covers NSFW, violence, and deepfakes, and it is sold as an API to platforms doing moderation at scale. The classifier is aggressive and well-calibrated.

On our corpus, Hive caught Midjourney v7 at an average confidence of 0.95, DALL-E 3 at 0.94, and Flux 1.1 at 0.92, the strongest across the board. After processing through Undetectr, the same images dropped to an average of 0.17, below the flag line for every platform we have data on.

Use it for: understanding how production moderation behaves. Its public demo is the best free proxy for a real upload screen. Limitation: no meaningful self-serve free tier for volume work.

2. Sightengine

Sightengine is a moderation API with a clean, well-documented AI-image endpoint. It is popular with mid-size platforms that want moderation without building it in-house. Accuracy tracks close to Hive: 0.93 on Midjourney, 0.92 on DALL-E, 0.89 on Flux.

Flux 1.1 was consistently the hardest model for every detector to catch, and Sightengine was no exception; its Flux score sat a few points below its Midjourney number. That pattern held across the whole benchmark and is worth remembering if Flux is your generator of choice.

Use it for: developers adding an AI-image screen to an existing product. Limitation: API-first, so not the tool for a one-off "is this image AI" check.

3. Illuminarty

Illuminarty is the research-flavoured option. It exposes not just a yes/no score but a heat-map of which regions of the image drove the classification, which makes it the most useful tool here for understanding why an image reads as generated. That transparency is genuinely rare.

On our corpus it scored Midjourney at 0.91, DALL-E at 0.90, and Flux at 0.86. Slightly behind Hive and Sightengine on raw accuracy, but the region-level output is a real differentiator for anyone auditing images rather than just screening them.

Use it for: forensic-style checks where you need to see the evidence, not just the score. Limitation: the free tier is rate-limited, and Flux accuracy trails the leaders.

4. AI or Not

AI or Not is the best-known consumer checker in the category. Drop an image in, get a verdict back in a few seconds, no account needed for the free tier. It is fast and friendly, and it is what most people mean when they type "ai art detector" into a search box.

It is also more permissive than the moderation APIs. On our corpus it caught Midjourney at 0.88 and Flux at 0.83, accurate enough to flag obvious AI images but low enough that lightly edited or upscaled output can slip under a strict platform threshold. Tracks that pass AI or Not do not always pass a stock library's in-house screen.

Use it for: quick sanity checks on a single image. Limitation: a pass here is not a guarantee of a pass on a production classifier.

5. Optic

Optic is the lightweight, browser-first checker at the permissive end of our set. It is free and quick, and for casual "is this image ai" curiosity it does the job. But it was the softest detector we tested: 0.85 on Midjourney, 0.84 on DALL-E, and 0.79 on Flux, the only sub-0.80 score in the raw column.

That permissiveness means more false negatives on borderline images. Optic is fine as a first glance and unreliable as a publish-readiness gate.

Use it for: a fast, no-friction first look. Limitation: the most likely of the five to miss a real AI image, especially Flux output.

What the data actually says

The headline finding mirrors what we found on the audio side. All five detectors, regardless of tier or target market, share the same source feature: the frequency-domain and statistical signature that generative image models embed during synthesis. They differ in threshold tuning, in API surface, in price, in how much they surface to the user, but they are all reading the same fingerprint.

This means three things in practice.

First, beating any one of these detectors does not generalise. If you find a quirk that gets an image past Optic, that trick will not work on Hive, and it will not work on a stock library's in-house screen. The detectors are different products built on the same underlying signal.

Second, removing the underlying signature does generalise. The Undetectr-processed images in our benchmark dropped below 0.20 on every detector at once, Hive, Sightengine, Illuminarty, AI or Not, and Optic. The remover did not have to be tuned for any specific detector. Removing the signature once removes it from all of them. We saw the identical pattern in our AI music detector benchmark, which is what convinced us the effect is structural rather than a single-detector fluke.

Third, the relevant question is not "which detector should I check my image against." It is "is the signature still in my file." If yes, every detector will flag it sooner or later. If no, none of them will.

Where Undetectr fits

Undetectr is not a detector. It is the remover on the other side of the problem, and it is the reason the final row of our comparison table reads the way it does.

It runs in the browser, takes roughly 90 seconds per image, and handles Midjourney v7, DALL-E 3, and Flux 1.1 output directly. Rather than editing pixels you can see, it rewrites the frequency-domain signature the classifiers depend on. Across our 50-file corpus it holds a pass rate near 98 percent, and on the image subset every processed file scored below 0.20 on all five detectors here. It is $39 one-time on the Lifetime tier (a $19 Starter tier gives 10 credits), and the company has publicly signalled a rise to $99.

If the visible watermark is your concern rather than the invisible signature, that is a different job; our Midjourney watermark remover and AI watermark detector guides cover the metadata-and-mark layer specifically.

How to use these detectors in practice

For most creators, the workflow is straightforward.

Step 1. Generate or finish your image in your model of choice: Midjourney, DALL-E, Flux, Stable Diffusion.

Step 2. Run the file through Undetectr or a comparable artifact remover. We benchmarked the alternatives in our AI watermark remover guide; Undetectr was the only tool that cleared every detector.

Step 3. Pre-screen the cleaned image with AI or Not's free tier or Illuminarty's web checker. If the score is below 0.40, you are clear. If it is above 0.70, run the file through the remover again; there are edge cases where a single pass leaves residual signal.

Step 4. Publish or submit.

The detectors are not your enemy. They are a useful pre-flight check before you commit to a release. Use them as the diagnostic tool they are.

What people search for that nobody answers well

The keyword data we pulled for this guide shows a familiar gap. People search for "ai image detector," "detect ai images," and "ai generated image detector" at meaningful volume, but the top results are dominated by either thin SEO pages selling generic detection services or first-party marketing from the detector vendors themselves.

There is no honest, independent comparison of how the five public detectors actually perform on a controlled image corpus. The vendors do not publish that data, because it would expose how much they overlap. This guide is our attempt to be the missing comparison. Every detector was tested at retail tier, every image was generated fresh, and we will keep the page current as new detectors ship and existing ones retune.

The category outlook

Three things are likely over the next twelve months.

First, the detectors will improve. Training corpora are getting richer, false-positive rates are falling, and threshold tuning is getting more nuanced. The permissive end of this list, Optic and AI or Not's free tier, will either catch up or get replaced.

Second, model watermarking gets deeper. Midjourney, OpenAI, and Black Forest Labs are all shipping into a regulatory environment that increasingly expects provenance signals, and C2PA content credentials plus stronger embedded signatures are becoming standard. The signature the classifiers read is getting more distinctive, not less.

Third, and because of that, the gap between a generic watermark remover and a true artifact remover like Undetectr will widen rather than narrow. Tools that only strip visible marks or metadata will become less useful. Tools that rewrite the underlying signature will become essential.

For now, the recommendation is unchanged. Run your image through Undetectr before it sees a detector. Pre-screen with AI or Not or Illuminarty. Then publish.