Is This Image Photoshopped?

A Photoshopped image starts life as a real photograph. Someone opened it and changed part of it: removed a person, smoothed a face, merged two shots, moved an object. Most of the pixels are still camera output. The forensic signals are local, and they are about inconsistency: this region was compressed a different number of times from that one, this shadow disagrees with that light source.

An AI-generated image was never a photograph. There is no camera, no lens, no sensor, and no original. The signals are global and they are about origin: the frequency signature of the model that produced it, the absence of sensor noise, EXIF that was never written because no hardware wrote it.

Which matters depends on what you are trying to establish. If you want to know whether a news photograph has been doctored by hand, the manual checks on this page are where to start. If you want to know whether an image was generated or changed with AI, that is what the FauxLens scan answers, and our AI image detector page covers it in depth.

The awkward middle case is generative fill, where someone uses AI inside a real photograph to extend a background or remove an object. That is both things at once: a genuine photo with synthetic regions inside it. The scan does not give it a separate verdict: it comes back as AI-generated, with an evidence line noting that an AI tool altered only part of the image and the rest may be a genuine capture.

• Careful edits: the scan gives no location for an edit and can miss a skilled one. A photo edited and re-saved at very high quality, Q95 and above, can leave a compression difference too small to measure, and skilled editors know it.
• Screenshots of photos: a screenshot flattens everything into one new compression pass with one uniform history, which erases much of the evidence an edit leaves. If someone sends you a screenshot rather than the file, that is worth noticing on its own.
• Social media copies: Facebook, Instagram, WhatsApp and X re-compress on upload, sometimes more than once. Analysis still works, but confidence drops with each round trip, so ask for the original file where you can.
• Small edits: removing a spot, whitening a tooth or nudging a horizon sits below what pixel forensics can reliably separate from ordinary camera noise.

And the limitation that matters most: detection tells you an image was edited, never why. A magazine cover has been retouched. So has almost every product photograph, every headshot and most holiday snaps. Editing is not evidence of dishonesty. Whether an edit is deceptive depends on context that no forensic tool can see.

The tells were visible without any tooling: a misaligned sleeve, a hand that did not resolve, a zip that failed to line up. The Princess of Wales later said she had edited it herself. The interesting part is not the edit, which was trivial, but the reaction, which was not: four agencies decided that a photograph they could not vouch for was worse than no photograph.

The pattern repeats at lower profile constantly. Property listings with skies replaced and cracks removed. Dating profiles reshaped past recognition. Marketplace listings where the damage has been cloned out. Insurance claims with the wrong car. Court exhibits where a timestamp has been altered by a single digit.

What these share is that the edit is usually small and the consequence is not. Nobody fabricates an entire scene. They change one number, remove one object, smooth one detail, and rely on nobody looking closely enough to check.

An amateur edit is loud. Someone opens a JPEG, uses the clone stamp on it, saves it back over itself at whatever quality the dialog defaults to, and ships it. That leaves an obvious double-compression boundary, often visible clone patterns, and metadata openly naming the software that did it.

A professional works differently. They edit non-destructively in a raw workflow, keep everything on layers, export once to a final file, and never re-save on top of an existing JPEG. That single export gives the whole image one uniform compression history, which is exactly what removes the ELA signal. Add deliberate grain to mask noise-floor differences and you have suppressed most of the pixel-level evidence on purpose.

What survives is physics. You can flatten compression history, but you cannot make a pasted shadow agree with a light source that was never there. Perspective has to be consistent across a composite, and reflections have to match the geometry of the scene. These take real effort to get right and are where careful composites usually fail.

Sensor noise is the other durable one. Every camera sensor has a unique noise fingerprint, and it is present across the whole frame of a genuine photograph. Content pasted in from a different camera carries a different fingerprint, or none at all. Uniform grain added over the top does not fix that, because the underlying inconsistency is still there beneath it.

In practice, a clean scan is not the end of the story. If the shadows, perspective or reflections do not add up when you check them yourself, that usually means someone competent has been at work.