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Use our AI photo detector and image authenticity checker to analyze AI generated, edited, or manipulated images.
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JPG, JPEG, PNG or WebP · Maximum 10 MB
An AI Image Detector is an image analysis tool that estimates whether a file shows indicators associated with generative AI, editing, manipulation, or a conventional camera workflow.
How our analysis worksOur AI generated image detector is designed for first-pass investigation. It measures what the submitted file can actually reveal: encoding, dimensions, metadata, pixel variation, edge behavior, color distribution, noise estimates, software strings, and readable provenance markers.
The result is not a binary accusation. It is a structured probability report for journalists checking a circulating photo, teachers reviewing visual assignments, creators protecting a workflow, photographers assessing a downloaded file, and everyday users asking, “Is this image AI generated?”
A careful verdict comes from several modest signals working together—not from one dramatic clue.
The server verifies size, declared type, filename extension, file signature, decodability, and safe dimensions.
It parses metadata and measures compression ratio, pixel variation, edges, noise behavior, and color diversity.
Seven signal families contribute documented weights to AI, editing, manipulation, and authenticity probabilities.
You receive a verdict, confidence level, score breakdown, evidence lists, metadata table, and limitations.
Reviews dimensions, embedded software references, pixel statistics, and other patterns sometimes associated with synthetic media.
Parses available EXIF and XMP fields, including camera details, dates, creator tools, orientation, and GPS presence.
Looks for software labels, encoding mismatches, resampling clues, and evidence consistent with conventional or AI-assisted editing.
Relates file size to pixel count and tests whether the encoding profile sits outside a typical middle range.
Produces a cautious deepfake probability informed by the same measurable file and pixel indicators—not face recognition.
Balances provenance, camera attribution, file integrity, and manipulation risk into an interpretable authenticity score.
Searches the readable file structure for C2PA and Content Credentials markers while explaining what absence does—and does not—mean.
Combines editing, recompression, noise, metadata, and structural inconsistencies into a separate manipulation score.
Real camera files often carry a coherent chain of lens, exposure, sensor, date, and color-profile information. Synthetic images may instead show generator labels, grid-friendly dimensions, unusually smooth regions, or export-only metadata.
Yet those tendencies are not rules. A screenshot of a genuine photograph can lose camera metadata; a carefully exported AI artwork can resemble a standard edited file. That is why this real vs AI image detector exposes multiple scores and the evidence behind them.
Compare AI images with real photosAn image manipulation detector should distinguish evidence of editing from evidence of full synthesis. This photo authenticity checker therefore reports traditional editing, AI editing, manipulation, and deepfake probabilities separately.
Learn to detect edited photosThe analyzer can surface readable references associated with Midjourney, DALL E, Stable Diffusion, Adobe Firefly, ChatGPT image generation, and other generative systems.
When a file contains an explicit software label or workflow string, the report identifies that text as an important signal. It does not mean this website called that provider, and it does not prove the entire visible image came from that system. Metadata can be copied, changed, or removed.
Analysis is performed by the local server-side heuristic pipeline described on this page.
Forensic signals weaken each time a file is resized, screenshotted, compressed, stripped of metadata, or passed through another editor.
Generative systems also evolve quickly, while genuine cameras increasingly use computational photography. As these workflows converge, a responsible fake image detector must allow “Inconclusive,” publish confidence separately from probability, and encourage source verification.
Review our image analysis limitationsNo. A responsible detector estimates probability from available signals. Screenshots, platform recompression, metadata removal, and new generation methods can hide useful evidence. Treat the score as a triage aid, then examine provenance and context.
The file is sent only when you select Analyze Image, processed in server memory, and not written to a public folder or permanent image store by this application. The response contains measurements, not a hosted copy of your picture.
A real photo may lack EXIF data, use square dimensions, pass through editing software, or be heavily compressed—signals that also occur in synthetic workflows. The report separates these clues so you can see why uncertainty exists.
No. Messaging apps, social networks, screenshots, export tools, and privacy settings routinely remove EXIF. Missing metadata lowers the amount of evidence available; it is not proof of AI generation or manipulation.
It can report generator names when readable metadata or file text contains them. Without an explicit marker, it does not claim to name the source model because pixel characteristics overlap and exports can remove attribution.
AI generated usually means most of the visual content was synthesized. AI edited means a real or existing image may have been altered with generative fill, replacement, enhancement, or another assisted operation. The boundary is sometimes impossible to recover from a final flattened file.
The score balances AI probability, manipulation probability, deepfake risk, camera attribution, provenance markers, and file integrity. It is intentionally not the simple inverse of one detector score because several explanations may fit the same file.
No. Valid Content Credentials can document origin and edits, which is valuable, but their meaning depends on the signed assertions and trusted chain. A missing credential proves nothing; a present marker still deserves proper validation.
Measurements and weights are deterministic. Identical bytes lead to the same metadata, pixel sample, encoding measurements, fingerprint, and score. A re-exported copy can differ because its bytes and forensic signals have changed.
Use qualified human analysis for legal disputes, safety decisions, high-impact journalism, criminal allegations, employment actions, or any situation where the cost of an error is significant. Preserve the original file and chain of custody whenever possible.
A repeatable process for checking visual clues, metadata, and automated scores.
Learn what lighting, hands, text, reflections, texture, and provenance can reveal.
Understand recompression, copy-move edits, AI alteration, and tampering limits.
Use EXIF fields and Content Credentials without overinterpreting missing data.
Upload a JPG, PNG, or WebP file to review metadata, encoding, pixel, compression, and provenance indicators in one private analysis.
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