Free AI Image Detector

Think an image may have been created or heavily modified with artificial intelligence? Upload it to NetQuill’s free AI image detector to screen it for AI-generation signals and available provenance clues.

AI Image Detector

Upload an image to check if it's AI-generated or natural.

Click to upload or drag & drop

Supports JPG, PNG, WEBP

Analyze Image

Verdict 0%

Important: AI-image detection is not definitive. A detector can produce false positives and false negatives, particularly after editing, resizing, screenshots or compression. Treat the result as a signal for further investigation rather than proof of how an image was created.

How the AI Image Detector Works

User selects an image.

The image is analyzed by the visual detection model.

The tool evaluates patterns associated with synthetic imagery.

Available file/provenance information can provide additional clues.

The tool produces a result.

The result should be interpreted as evidence, not absolute proof.
When you upload an image, the detector examines the visual patterns and statistical fingerprints that AI image generators tend to leave behind. Modern tools like Midjourney, DALL-E, Stable Diffusion, and Adobe Firefly each produce images using diffusion model architecture — a process that, despite producing highly realistic results, leaves subtle traces in pixel-level data, texture distribution, and edge patterns that differ statistically from photographs taken by a real camera.

The detector does not make a binary yes-or-no decision. Instead, it produces a probability score — a percentage that reflects how strongly the visual evidence points toward synthetic generation. A score above 80% is a strong signal worth investigating further. A score below 30% suggests the image is more likely authentic, though not guaranteed. Scores in the middle range are genuinely uncertain, and that honesty is intentional — in 2026, the generators have become good enough that confident certainty is often misleading. We would rather give you an accurate probability than a false definitive answer.

Can AI Image Detectors Be 100% Accurate?

No AI image detector can guarantee that every image will be correctly classified. AI-generated images can be edited, resized, compressed, screenshotted or otherwise transformed, which can affect detection. Conversely, some genuine photographs can contain visual characteristics that cause a detector to produce a false positive.

The core difficulty is what researchers call the generator-detector arms race. Every time detection tools improve at identifying a particular generation technique, the model developers release a new version that produces images with fewer of those telltale patterns. An independent benchmark published on arXiv in 2025 tested sixteen different AI image detectors against images produced by generators from different eras — and found mean accuracy of around 75% against 2020-era models, dropping to roughly 38% against 2024 models, and between 18% and 30% against modern commercial generators like Flux Dev and Midjourney when those images had been further processed, compressed, or resized.

This is not a reason to stop using detection tools — it’s a reason to use them as one layer of a broader check rather than a final authority. The NetQuill AI Image Detector is designed with this limitation built into how it communicates results. You will always see a probability signal alongside an explanation of what that score means, rather than a confident verdict that could give you false certainty in either direction. For the highest-stakes situations — verifying a news photograph, checking an image involved in a legal or financial matter — combine the detector result with a Content Credentials check at contentcredentials.org and a reverse image search through Google Images or TinEye.

How to Tell If an Image Was AI-Generated

The most reliable approach in 2026 is to work through a layered checklist rather than relying on any single method. The old visual tells — counting fingers, looking for distorted ears, checking whether text in the image made sense — mostly stopped working as generators improved through 2024 and 2025. Modern AI image tools handle hands and facial features convincingly enough that a clean result no longer proves anything. What still works is combining multiple independent checks: a detection tool, a provenance check, a reverse image search, and a careful look at the parts of the image generators still struggle with, like background textures, object interactions, and reflection consistency. The steps below walk through each layer in order of reliability.

1. Check the image with an AI detector

Open the Free AI Image Detector Tool

2. Examine hands and fingers

Not because this is definitive, but because AI-generated imagery can sometimes contain anatomical inconsistencies.

3. Look at text

AI-generated images can sometimes contain distorted or nonsensical text.

4. Examine reflections and shadows

Look for inconsistencies.

5. Check image metadata

Metadata may contain useful clues, although it can easily be removed.

6. Look for Content Credentials

Where available, provenance information can provide stronger evidence than visual guessing alone.

This makes the page genuinely useful rather than just being a wrapper around a tool.

AI Detection vs Image Metadata

AI detector:

An AI image detector analyses the visual content of the image itself — the pixel patterns, texture distributions, frequency signatures, and structural characteristics that differ between photographs taken by a camera and images generated by a diffusion model. The advantage of this method is that it works on any image file regardless of what other information is attached to it. The limitation is accuracy: detection tools are probabilistic, they can produce false positives on heavily edited real photographs, and their accuracy drops on images that have been compressed or processed after generation. Use the detector as your first check and your fastest signal — then verify with additional methods if the result matters.

Metadata:

Image metadata — specifically EXIF data — is the invisible layer of information that digital cameras embed in every photograph they produce. A real photograph from a smartphone or camera typically contains the device make and model, lens settings like aperture and shutter speed, the exact date and time of capture, and sometimes GPS coordinates. AI-generated images typically produce files with either no EXIF data or generic software tags rather than camera-specific records. The significant catch is that metadata is fragile and easily lost — Instagram, WhatsApp, Facebook, and most messaging apps automatically strip metadata from every image on upload. A real photograph shared through social media will arrive with no EXIF data, making it indistinguishable from a generated image on that measure alone. Missing metadata means “keep investigating,” not “this is fake.”

Content Credentials/C2PA:

Content Credentials are the most reliable method currently available because they work differently from both visual detection and metadata analysis. Instead of trying to detect AI generation after the fact, Content Credentials create a cryptographically verified record at the point of creation — a tamper-evident provenance trail showing which tools created or modified the image and whether AI generation was involved. The Coalition for Content Provenance and Authenticity (C2PA) maintains the open standard, which is now supported by Adobe, Google, Microsoft, OpenAI, Meta, and over 6,000 member organisations. Compatible cameras including recent Google Pixel, Samsung Galaxy, and Leica models can embed credentials into photographs at the moment of capture. To check for Content Credentials, upload an image to contentcredentials.org/verify — if valid credentials are present you will see a verified record of the image’s origin. If no credentials appear, that is not proof the image is real; credentials can be lost when images are screenshotted, converted, or uploaded to platforms that do not preserve them.

Frequently Asked Questions

Why AI Image Detection Matters in 2026

The ability to check whether an image is AI-generated has moved from a niche technical concern to an everyday practical need — faster than most people anticipated.

A 2026 report by Malwarebytes found that 85% of people now agree it is hard to tell AI scams from real content, up from 66% just one year earlier. One in five adults has experienced some form of AI-generated deception in the past twelve months. Romance scams using AI-generated profile pictures have cost Americans over $650 million according to FBI data. Fabricated images have been used to manipulate financial markets, spread political disinformation before elections, and create non-consensual explicit imagery of real people.

The problem extends beyond individual harm. When fake images circulate as real news photographs, they erode the shared foundation of factual information that public debate depends on. When AI-generated faces are used to build fake professional profiles, they undermine trust in the digital identities we rely on for hiring, partnerships, and online communities.

None of this requires sophisticated technical knowledge to defend against — it requires knowing that the problem exists and having a fast, free way to check. That is what this tool is for. It will not catch every AI-generated image, and it will say so honestly when it is uncertain. But for the vast majority of suspicious images you encounter day-to-day, a ten-second check is enough to tell you whether further investigation is warranted.

AI image generation is not going away, and it will keep improving. The practical response is not to distrust every image you see — that leads to its own problems, including dismissing genuine evidence as fake. The practical response is to know when to check, how to check, and how to read the results accurately. This tool is one part of that.