Q1
What is an AI image detector?
An AI image detector (also called an AI image checker or AI photo detector) analyzes the pixels, metadata and compression history of an image to estimate whether it was generated by an AI model such as Midjourney, DALL-E, Stable Diffusion or Flux — or captured with a real camera. GRAIN is a free AI image detector that runs entirely in your browser.
Q2
How does this AI image detector work?
The tool decodes your image locally and runs more than ten forensic modules: EXIF and C2PA provenance analysis, sensor-noise fingerprinting, pixel-neighbor statistics, color-channel correlation, high-frequency energy mapping, JPEG block and resampling analysis, a regional anomaly heatmap, and multi-scale consistency checks. All evidence is fused into a single AI-likelihood percentage with a REAL / AI / EDITED split.
Q3
How can I tell if a picture is AI-generated for free?
Upload it here — this free AI image checker requires no sign-up and nothing is uploaded to any server. It examines sensor-noise fingerprints, high-frequency pixel statistics, EXIF metadata, C2PA provenance and compression history, then reports an AI-likelihood percentage. Manual inspection helps too, but eyes alone are unreliable: modern generators rarely leave the obvious artifacts people expect, which is exactly why pixel-level forensics matters.
Q4
What are the most common visual signs of an AI-generated image?
Classic generative tells include extra or malformed fingers, teeth that merge together, text that looks almost-but-not-quite readable, mismatched earrings or glasses, inconsistent lighting and shadows, hair that melts into the background, and backgrounds that resemble painted blur rather than optical bokeh. Modern models have fixed many of these flaws — so a clean-looking image is not automatically real. Visual inspection is a first pass; forensic analysis is the second.
Q5
How accurate is AI image detection?
Strong generative signatures — embedded AI tool markers, dead high-frequency zones with synthetic texture — are detected with high confidence. But no AI image detector is 100% accurate: heavy JPEG compression, screenshots, denoising and platform re-encoding can hide or mimic forensic signals. Treat every result as probabilistic evidence, never as proof.
Q6
What does a 90% AI score mean?
When high-frequency analysis finds a strong generative signature — high-frequency energy concentrated in some regions while others are statistically dead — the final AI likelihood is automatically locked at 90% or higher. This means the pixel evidence is strongly consistent with AI generation, but it is still an evidence-based estimate, not mathematical proof.
Q7
Can AI image detectors be fooled?
Yes. Heavy JPEG compression, resizing, re-encoding, screenshots, deliberate noise injection and adversarial perturbations can all weaken or destroy the forensic signals detectors rely on. In the other direction, heavily denoised real photos can look statistically "too clean". This is why this tool reports a confidence level, a three-way split and explicit limitations with every analysis — instead of a naive yes/no answer.
Q8
Why did a real photo get a high AI score?
Heavily denoised photos, studio shots at base ISO, screenshots, upscaled images and repeatedly re-compressed social-media downloads can all lack the sensor-noise signatures of typical camera capture. This is why the tool reports confidence levels and limitations instead of a binary yes/no verdict.
Q9
Can the tool detect which AI model created an image?
Sometimes. When the file still carries generator-specific traces — Stable Diffusion's "parameters" text chunk, ComfyUI workflow data, Midjourney metadata, or a C2PA manifest naming the tool — the detector reports the exact pipeline that wrote the file. Once those markers are stripped by re-saving or platform re-encoding, the tool can still classify the image as consistent with AI generation from its pixel statistics, but cannot name the specific model.
Q10
What's the difference between AI-generated and AI-edited images?
AI-generated images are created entirely by a model from a prompt — no camera-captured content exists in them. AI-edited images are real photographs modified with AI: generative fill, object removal, sky replacement or AI upscaling. The evidence signatures differ — a fully synthetic image deviates globally, while a local edit leaves an anomaly cluster — which is why this detector reports a three-way split (AI-Generated % / Edited % / Real %) rather than one number.
Q11
Can AI-generated faces and deepfakes be detected?
AI-generated portraits are often detectable with the same pixel forensics this tool applies: synthetic skin texture, missing sensor noise in facial regions, and high-frequency inconsistency between face, hair and background. Still-image deepfakes follow the same rules. For video deepfakes, temporal frame-to-frame analysis is required, which is beyond this tool's still-image focus.
Q12
Do AI-generated images contain EXIF metadata?
Usually not — or only partially. Generators like Midjourney, DALL-E, Stable Diffusion and Flux export without camera EXIF fields (lens, focal length, exposure) because no physical camera was involved. Some embed their own markers — prompt, seed, sampler settings or a C2PA manifest — which this detector scans for at byte level. Important caveat: social platforms strip EXIF from real photos too, so missing metadata alone is never proof of AI generation.
Q13
What is C2PA / Content Credentials?
C2PA (Coalition for Content Provenance and Authenticity) — marketed as Content Credentials — is an industry standard for cryptographically signed provenance metadata. Supported cameras (Leica, Nikon, Sony) and editing tools (Photoshop, Firefly) can embed a signed manifest recording how an image was created and what was done to it. A valid C2PA manifest is among the strongest provenance evidence available; this detector reports its presence, though full cryptographic chain validation happens outside the browser.
Q14
How is this different from reverse image search?
Reverse image search (Google Lens, TinEye) finds visually similar images elsewhere online — excellent for tracing the origin or earlier versions of an image, but useless for an original photo that has never been published. Pixel forensics works the opposite way: it examines the internal evidence of the file itself — noise, texture, compression, metadata — whether or not the image exists anywhere else online. For serious verification, use both together.
Q15
Does this AI image detector work on screenshots?
Yes, with reduced reliability. A screenshot re-renders the image through the display pipeline and typically re-compresses it, destroying the original file's metadata and most of its compression history. Pixel statistics such as noise structure and high-frequency patterns partially survive, so analysis still produces results — but treat screenshot findings with lower confidence and prefer analyzing the original file whenever possible.
Q16
Does the AI image detector work on iPhone and Android?
Yes. The detector is fully responsive and runs in any modern mobile browser on iPhone and Android — no app install, no account. Because all analysis runs on-device via the Canvas API, photos stay on your phone and results appear in seconds.
Q17
Which image formats are supported?
JPEG, PNG, WebP, GIF, BMP, SVG, AVIF, ICO, TIFF and HEIC/HEIF, plus camera-RAW files (CR2, CR3, NEF, ARW, DNG, ORF, RAF, RW2) analyzed through their embedded previews.
Q18
Does the AI image detector upload my photos?
No. All analysis is 100% local, using the browser Canvas API and ImageData. Your images never leave your device, there is no account, no upload and no tracking of your files.
Q19
Can I use the results as proof that an image is fake or real?
No. Results are probabilistic forensic evidence — consistent with a hypothesis, never proof of it. For journalistic, legal or academic use, combine this tool with independent verification: obtaining the original file, validating C2PA credentials, reverse image search, and professional forensic analysis. No automated detector — including this one — is accepted as standalone proof.
Q20
Is this AI image detector free?
Yes — unlimited detections, no sign-up, no watermark and no uploads. The app also works offline once loaded, apart from the optional sample photo and the on-demand TIFF/HEIC decoders.
EXPECTED · LOW AI %