How to Spot AI-Generated Images: A Practical Guide

In 2026, image generators can produce pictures that are very hard to tell apart from real photographs. Six fingers, garbled text and "plastic" skin show up less and less often. At the same time, the amount of AI-generated content online keeps growing, and trust in images keeps falling.

For businesses, this is not a theoretical question. A supplier sends photos of a product that may not exist. A customer attaches "proof" to a complaint. A competitor posts a review with a fabricated photo. Someone on your marketing team uploads an image to the website that looks just a little too perfect. In every one of these cases, you need to know how to check.

In this guide we cover:

• why AI images are getting harder to spot;
• which clues you can still see with the naked eye, and which parts of an image deserve the closest look;
• how to check metadata and the origin of a file;
• what Content Credentials and SynthID are, and how to verify them;
• what AI detectors can and cannot do;
• what a complete step-by-step check looks like;
• how a business can use AI images without damaging customer trust.

Why the naked eye is no longer enough

Modern image models are trained on enormous volumes of real photographs. They have learned the statistical patterns of lighting, skin texture, reflections and perspective far better than earlier generations did. Google's Imagen and Nano Banana, OpenAI's image models, Adobe Firefly, Midjourney and Flux have all gone through several rounds of training with one shared goal: remove the obvious flaws.

As a result, the bugs everyone used to joke about, such as a sixth finger, crooked teeth or jewellery melting into skin, have become rare in ordinary camera angles. Text on signs is legible, hands look anatomically plausible, and lighting is consistent within the frame. An untrained person looking only at the image itself, without context or extra data, is essentially guessing.

Even so, the models still do not "understand" how a photograph is made. They predict what pixels should look like; they do not simulate a real camera in a physical world. That leaves subtler inconsistencies behind:

• errors in perspective;
• reflections that do not match the scene;
• odd texture transitions in the background;
• unnatural smoothness, or the opposite: too much detail in places where a real camera would produce sensor noise;
• unnatural shadow depth.

These signals are weaker than they used to be, so you need to check for them methodically instead of trusting your first impression.

Why this matters for your business

Most guides to spotting AI images focus on fake news. A business has its own, very practical reasons to check images:

• Suppliers and products. When you order from a new supplier, at home or abroad, the "factory photos" may be generated. A product that looks flawless in the picture can arrive looking completely different.
• Reviews and complaints. A photo of a damaged product or a "stain on the shirt" attached to a complaint or an online review can now be produced in seconds.
• Candidates and partners. Profile photos, portfolios and "proof" of completed projects can all be fabricated.
• Fraud. Fake images of documents, deliveries or senior executives are used in phishing and social engineering. We cover this in our guide to information security threats for businesses.
• Your own content. If your marketing team or a freelancer publishes an AI-generated image on your website or in a campaign and presents it as a real photo of your product or team, the responsibility is yours.

That is why it makes sense for at least one person in your company to know how to work through a suspicious image in a fixed order, rather than relying on gut feeling.

How to spot an AI image by eye

Visual analysis is the simplest and fastest place to start. Look at the image as a whole first, and only then move on to the details.

Modern generators handle a simple portrait with nothing around it, or a basic scene, very well. They struggle more with groups of people, hands holding objects, small text and complex perspective. Images like these deserve extra attention.

A few general clues to look for:

• Sharpness that is too even. The whole frame is equally sharp from foreground to background, as if the lens never "chose" a focal plane.
• No sensor noise. A real photo almost always has some noise in the dark areas. In an AI image, the shadows are sometimes perfectly clean.
• Artificial grain. Grain is sometimes added to make an image look like a photo, but it does not fit the rest of the picture: the same grain appears in bright and dark areas alike.
• No traces of optics. Real photos usually show slight darkening in the corners (vignetting), colour fringing at the edges and mild lens distortion.
• A 3D-render look. Plants, fabrics and surfaces look too "clean", more like a visualisation than the real world.

Then zoom in to 100–200% and work through the image area by area. One strange finger proves nothing on its own: similar defects sometimes appear in real photos that have been heavily processed with AI tools, for example background removal or upscaling. But if you find several independent oddities, you have a reason to keep checking.

Which parts of the image to check

There are a few areas where image generators make the most mistakes. Go through them in order.

Hands

Even in 2026, complex poses and hands gripping objects remain a weak spot. Do not just count fingers. Check the joints, the nails, the proportions between finger segments and the way the fingers hold an object. Sometimes a finger passes straight through a cup or a phone; sometimes the grip is simply physically impossible. Rings may have the wrong geometry or appear to sit on top of the knuckle itself.

Eyes and faces

Compare the reflection of the light source in the left and right eye: both should match the same light. Pupils should look the same under the same lighting. Differences are possible, but usually only in very complex lighting, such as at a concert.

Pay attention to the skin. Skin that is too smooth and has no pores, or an odd texture where skin meets hair, is a warning sign. In AI images, teeth can merge into one another, and ears and earrings can come out asymmetrical or unfinished.

Text, signs and logos

Large text is no longer a problem for generators, but small text still is. When you zoom in, letters warp, the typeface "drifts", and soft halos appear around the characters. Logos sometimes come out with the wrong proportions or with extra elements. The most basic bug is a random string of letters where a business name should be. With less common languages and non-Latin scripts, which are under-represented in training data, this clue is even more obvious: mirrored letters, wrong text direction and words that mean nothing.

Light, shadows and reflections

The direction of shadows should match a single light source, or several consistent ones. In AI images, shadows sometimes fall in different directions or are too soft for the light in the scene. Reflections in glass, water or metal may not match the scene, for example a reflection of a person with a different facial expression. Keep in mind that smartphone photos taken in HDR mode can also lose shadow depth, so this clue is not enough on its own.

Backgrounds and repeating patterns

Backgrounds in AI images are usually rendered with less care than the foreground. Repeating patterns such as tiles, bricks, wallpaper or crowds blur or break. In a crowd, you may see people with identical faces or identical clothes. Parallel lines, such as rails, floor lines or shelves, converge at the wrong angle or are cut off.

Hair, edges and fabrics

A generator sometimes struggles to separate an object from its background: the two merge, or the edge is too sharp, as if the object had been pasted in. Hair, jewellery, glasses, straps and light fabrics suffer most. Also check whether both arms of a pair of glasses match and whether a sleeve ends where it logically should.

Remember: none of these clues alone proves that an image was generated by AI. But if you collect more than two or three suspicious details in a single image, that is a reason to move on to a technical check.

How to check metadata and file origin

Metadata is one of the most useful technical signals. A real photo from a camera or phone usually contains EXIF data: the device model, lens, shutter speed, aperture, ISO, capture date (DateTimeOriginal) and sometimes GPS location. Nobody actually photographed an image that came out of a generator, so it has no such data.

What to look for

• The Software field. This usually shows the program used to edit the image, such as Photoshop. Some services now also write the name of the generator here.
• XMP and IPTC fields. Large platforms increasingly label images in the Digital Source Type field, which is part of the IPTC standard. The value trainedAlgorithmicMedia means the image was created by a model, while a value for AI-assisted editing means a real photo was altered.
• Complete, consistent camera data. A camera model, lens and settings that match each other make it more likely you are looking at a genuine photo.

Where to check

• In your operating system. On Windows, go to Properties → Details; on macOS, use Get Info or the Inspector window in Preview.
• In dedicated software. ExifTool is the most precise professional tool. digiKam and IrfanView are convenient if you prefer a graphical interface.
• In online EXIF viewers. Handy for a quick check, but do not upload sensitive company or customer files to them.

Why metadata is not enough

Missing metadata proves nothing. Social networks, WhatsApp, screenshots and re-saving as JPEG almost always strip EXIF and XMP data. On the other hand, full camera data can be faked too, and editing software lets you choose which data to keep on export. Metadata is a strong helper, not a verdict.

Digital watermarks: Content Credentials and SynthID

In 2026 there are two main types of content labelling: Content Credentials based on the C2PA standard, and invisible watermarks such as SynthID.

Content Credentials (C2PA)

C2PA is an open standard backed by companies including Adobe, Microsoft, Google, OpenAI and Meta. It adds a cryptographically signed record of the file's origin. The record can state that an image was created by a particular model or, conversely, that it was captured by a camera or phone and then edited in a particular program. Camera and phone makers are gradually adding support for it.

If anyone changes the file or the record after signing, the check fails. You can check the status with the Verify tool on the Content Credentials website or with browser extensions that read these records. If the tool shows a valid record stating that the image was created with AI, that is strong evidence. However, the record can be lost when an image passes through social networks or is re-saved.

SynthID

SynthID is a Google DeepMind technology. The watermark is embedded directly in the pixels rather than in the metadata, so it survives compression, cropping and re-saving better. It marks content created with Google's tools, and Google offers a dedicated checking tool, SynthID Detector, as well as the option of asking the Gemini app whether an image was made with its tools.

What the results mean

A positive result, meaning a watermark or a record indicating AI generation, gives you high confidence. A negative result does not prove an image is real: it could have come from a generator that does not label its output, been heavily edited, or arrived as a screenshot. Platforms use these labels too. Meta, for example, shows an "AI info" label on content detected through such signals or disclosed by the person who posted it.

AI detectors: when they help

There is a whole category of services that estimate the probability that an image was created with AI. Well-known examples include Hive, Sightengine and AI or Not. They are trained to pick up statistical patterns that are hard to see with the eye, and they usually do a reasonable job on original files.

But they have clear limitations:

• Compression and screenshots reduce accuracy. An image that has been through WhatsApp and Instagram is not the same image that came out of the generator.
• False positives. Detectors sometimes flag real photos as AI, especially photos with heavy in-phone processing, complex compositions or filters.
• New models. Every new generation of generators requires detectors to be retrained, so they are always slightly behind.
• A percentage is not a verdict. "87% AI" is a model's estimate, not proof.

So use detectors as a quick filter, not a final diagnosis. And if you are dealing with customer images or sensitive material, read the service's terms of use before you upload anything.

A step-by-step check: what to do with a suspicious image

A serious check combines several tools in a fixed order. Here is what the process looks like:

1. Get the original file. Ask for the file itself, not a screenshot. On WhatsApp, it is better to send it as a document so the image is not compressed.

2. Check Content Credentials with the Verify tool. A signed record indicating AI generation all but settles the question. A record indicating capture by a camera is a strong argument for authenticity.

3. Check for watermarks with SynthID Detector or Gemini, depending on the likely source. A positive result is a significant confirmation.

4. Open the metadata (EXIF, XMP, IPTC). Full camera data supports authenticity. A generator name or the value trainedAlgorithmicMedia strengthens suspicion. Missing data is a neutral result.

5. Run a reverse image search on Google Lens, TinEye or Bing. Find the earliest appearance, other versions and the original context. A real photo usually has a history.

6. Zoom in and check the details: hands, eyes, text, shadows, perspective, textures and background. Note any independent inconsistencies you find.

7. Run one or two detectors if needed, and interpret the result only together with the other signals.

8. Cross-check the context. Who published it, when, are there other photos of the same event, and does the image fit the story? You can ask a supplier for a short video of the product or a photo with a handwritten note showing today's date.

The strongest evidence is Content Credentials and a watermark. A combination of several visual flaws, empty metadata and no history strengthens suspicion. But the absence of all these signals still does not guarantee an image is real, especially if the file has passed through several platforms. When the decision matters, such as paying a new supplier or handling a complaint, it is better to ask for additional evidence than to rely on a single check.

How to use AI images in your business without losing trust

AI images are a legitimate and useful tool: blog illustrations, campaign backgrounds, concept visualisations and enhanced product shots. The illustrations in this article were created with AI too, which is why they are labelled "Illustration: RJL Studio" and are not presented as photos. The problem starts when a generated image is presented as a real photo in a place where the customer expects the truth.

Practical rules

• Real product, real photo. On a product page and in an ad, customers should see what they will actually receive. AI is fine for backgrounds and settings, not for changing the product itself. Misleading product imagery can also fall foul of consumer protection rules, such as the EU rules on unfair commercial practices. This is general information, not legal advice.
• Real people on your About page. Team photos, testimonials and reviews with AI-generated faces destroy trust the moment someone finds out.
• Label when required. Meta, YouTube and TikTok ask you to label realistic content that was created or altered with AI, and Google Ads has its own disclosure requirements for political advertising. Check each platform's current policy before launching a campaign. In the European Union, the AI Act also sets transparency obligations for synthetic content.
• Keep the provenance. Do not strip Content Credentials and metadata from your own files. When someone wants to verify an image, you will have something to show them.
• Keep a consistent style. Random AI images from several tools create visual chaos. Define one style and stick to it, as we explain in our guide to brand visual consistency.
• Check before publishing. Go through every image at full zoom: strange lettering, hands, logos and details that do not exist in reality. We explain how to write prompts that reduce these mistakes in our guides to writing AI prompts and AI photoshoots.

Common mistakes

• "No EXIF, so it must be AI." Most images on social networks have no metadata, including real ones.
• Trusting a single detector. A percentage from one detector is not evidence, and certainly not a reason to accuse a customer or supplier.
• Only looking at fingers. Generators rarely get those wrong anymore. Look at perspective, shadows, small text and backgrounds.
• Checking a screenshot. Every screenshot strips labels and lowers the quality of the check. Ask for the original file.
• No process. If every employee checks "by feel", the result depends on who is on shift. A short shared checklist solves this.

How RJL Studio helps you work with AI images safely

At RJL Studio we use AI to create illustrations, backgrounds and visual content for websites and campaigns, and we know where to draw the line. We build a consistent visual style for businesses, write AI image guidelines for your team, review every image before it goes live and make sure product, team and testimonial photos stay real. In campaigns, we make sure creatives comply with Meta and Google policies.

Want a consistent visual language for your website and social channels, with images your customers can trust? Read about our branding services and campaign and social media marketing, or get in touch and we will get back to you.

Frequently asked questions

Can you identify an AI image with 100% certainty?

Not always. Especially strong evidence is a valid Content Credentials record or a watermark such as SynthID indicating AI generation. Without such labels, you are making an assessment based on several signals together: visual flaws, metadata, reverse image search and context.

If an image has no EXIF data, was it made with AI?

No. WhatsApp, Instagram, Facebook and screenshots strip most metadata from real photos too. Missing EXIF is a neutral result. Only a generator name or the value trainedAlgorithmicMedia in the metadata strengthens suspicion.

How far can you trust AI detectors?

They are a good quick filter, but not evidence. Accuracy drops on compressed, cropped or edited images, and they produce false positives on real photos. Use one or two detectors and interpret the result together with your other checks.

Can a business use AI images on its website and in ads?

Yes, and AI images work very well for illustrations, backgrounds and visualisations. What matters is not presenting them as real photos of a product, your team or your customers, labelling them when a platform requires it, and checking the current Meta and Google policies before a campaign.

What should you do when a supplier sends suspicious photos?

Ask for the original files rather than screenshots and check them using the steps in this guide. If you are still unsure, ask for a short video of the product or a photo with a note showing today's date. A genuine supplier will do this without any problem.