How to Tell If an Image Is AI Generated: 15 Signs to Check

What You Will Learn

• Which visual irregularities deserve a closer look

• Why a single odd hand, shadow or word is not proof

• How to check context, earlier copies and file history

• When metadata, provenance and detector results are useful

• How to record a cautious conclusion

Start with context, not pixels

Before zooming in, write down the claim attached to the image. Is it presented as a news photograph, a product photo, a dating profile, an artwork or an advertisement? A genuine photograph can still mislead when it is old, cropped or paired with a false caption. Conversely, a synthetic illustration may be accurately labelled and entirely harmless.

Ask three opening questions:

1. Who published the image?

2. What event, place, person or product does it supposedly show?

3. What decision would you make if the claim were true?

The higher the stakes, the stronger the evidence should be. Do not accuse a person of deception or reject important evidence because one detail looks unusual.

15 signs to inspect

1. Anatomy that does not resolve

Count fingers only after tracing the hand from wrist to fingertip. Look for fused digits, missing joints, limbs that meet the torso strangely or ears with incompatible structures. Motion blur, occlusion, wide-angle lenses and medical differences can all create unfamiliar shapes, so anatomy is a prompt for further checks rather than a verdict.

2. Hands that interact incorrectly

Hands may be individually plausible yet fail when they hold an object. Check whether fingers wrap around a cup, instrument or phone with believable contact and pressure. Follow each visible finger; do not infer a failure merely because part of a hand is hidden.

3. Text that changes style or meaning

Read signs, labels, uniforms, screens and packaging letter by letter. Warning signs include a word that begins coherently and dissolves, inconsistent type within one sign, or lettering that does not follow a surface. Real photos can contain foreign scripts, stylised logos, shallow focus and compression, so search for the actual brand or location before deciding.

4. Reflections that disagree with the scene

Mirrors, windows, polished tables, spectacles and eyes should reflect some combination of the light sources and surrounding geometry. Compare the position, shape and brightness of highlights. Reflections need not be perfect mirror copies: curved, dirty or tinted surfaces alter them.

5. Shadows with incompatible directions

Identify the darkest cast shadows and trace them back toward likely light sources. Multiple directions can be legitimate when there are lamps, flash, reflected light or a cloudy sky. Treat only a cluster of unexplained inconsistencies as meaningful.

6. Lighting that ignores form

Look at how light falls across a face, clothing folds and nearby objects. A bright rim on one subject but not on objects beside it may indicate compositing or generation. Traditional editing can produce the same mismatch, so this sign does not distinguish AI generation from other manipulation.

7. Repeated details with small mutations

Generators may repeat windows, leaves, jewellery, crowd faces or food items while changing each copy slightly. Search broad textures at normal size, then zoom in. Repetition can also come from architecture, manufacturing, bokeh or intentional cloning in a traditionally edited photograph.

8. Background objects that melt together

Examine the spaces between chairs, vehicles, people and shelves. Look for objects that merge without a clear boundary, handles with no attachment, or roads that stop illogically. Background blur can legitimately erase detail; test the scene’s structure, not simply its sharpness.

9. Hair and jewellery edges that lose continuity

Follow individual locks, earring hoops, necklace chains and spectacle arms. They may disappear, change material or pass through skin. Portrait mode, denoising and beauty retouching often damage the same fine edges, so compare several regions before drawing an inference.

10. Skin texture that is too uniform—or too detailed

Synthetic portraits may show wax-like skin, repeated pores or sharp facial detail beside smeared ears and hair. Yet makeup, studio lighting, phone processing and social-media filters can produce similar results. Skin appearance is among the least reliable clues when used alone.

11. Perspective that changes within the frame

Extend the edges of tables, buildings and roads mentally. Parallel lines in a real three-dimensional scene normally follow a coherent projection, even with lens distortion. A chair viewed from one height beside a table viewed from another deserves investigation, but unusual lenses and panoramic stitching can also bend geometry.

12. Objects that do not obey relationships

Check contact, weight and cause-and-effect. Feet should meet the ground; straps should support bags; utensils should enter hands at usable angles. This “scene logic” check often reveals more than searching for cosmetic flaws.

13. Depth of field that selects the wrong regions

In a camera photograph, distance, aperture, focus point and computational processing shape what appears sharp. Be cautious when equally distant objects alternate between crisp and blurred without an obvious boundary. Phone portrait modes can make segmentation mistakes around hair, glasses and gaps, so do not confuse a computational-photography error with a fully generated image.

14. Metadata that conflicts with the story

EXIF can record a camera model, lens, exposure, date and sometimes location. The current EXIF standard is version 3.1, published in January 2026. A claimed press photograph exported by an editing program with no camera fields may justify more questions, but missing metadata proves nothing: websites, messaging apps, screenshots and exports commonly remove it. Metadata can also be changed.

15. Provenance or detector signals

Content Credentials can carry signed information about an asset’s origin and edits. A valid credential is useful evidence about the signer and recorded history, but it does not establish that the depicted event is true. Missing credentials are also neutral because adoption is optional and credentials can be lost.

An AI detector can inspect statistical patterns that people cannot see. Its result is a confidence or probability-based assessment, not conclusive proof. Compression, resizing, screenshots, new generator models and mixed real/AI edits can change performance; NIST recommends a layered approach rather than treating any single technique as comprehensive.

Run a layered verification workflow

Step 1: Preserve the best available copy

Download the original file when lawful and safe. Keep the URL, account name, caption and time you encountered it. A screenshot is useful for context but often loses metadata and image quality.

Step 2: Inspect at two scales

At normal size, judge scene logic, light and perspective. Zoom in for text, anatomy and fine edges. Avoid extreme magnification that turns normal JPEG blocks and sharpening halos into supposed evidence.

[ORIGINAL EXAMPLE OR SCREENSHOT TO BE ADDED]

Suggested original asset: an annotated comparison showing one ambiguous clue, one stronger cluster of inconsistencies and one genuine photo with a phone-portrait segmentation error.

Suggested alt text: “Ambiguous image clue, multiple AI-image inconsistencies and a genuine phone portrait blur error compared side by side.”

Step 3: Search for earlier versions

Use Google Lens or another reverse-image service. Google’s documented search-by-image workflow accepts an upload, drag-and-drop file or image URL. Crop around a distinctive person, building or product and repeat the search if the full frame returns weak matches.

Look for:

• The earliest credible appearance

• A higher-resolution or uncropped copy

• An original caption or creator account

• Fact checks or reporting that show the same scene

• Versions with different dates, locations or claims

Google’s “About this image” feature may show earlier uses and approximate first-seen information, but Google warns that associated metadata can be modified.

Step 4: Inspect metadata and provenance

Read EXIF, IPTC and XMP fields locally when possible, especially for private images. Check whether timestamps, software tags and device information are mutually consistent. Then use a Content Credentials verifier if a credential is present. Never upload a sensitive image to a third-party service without reading its retention and privacy terms; consult the site’s privacy information.

Step 5: Add a detector result

Have an image you want to examine? Use the AI Image Detector as one part of a broader verification process. Record the result, model or tool version if shown, input file and any warnings. A “likely AI” result should trigger source checks, not public accusation.

Step 6: State the conclusion at the right level

Use language that matches the evidence:

• No reliable indication found: not the same as proven authentic.

• Some suspicious features: further verification needed.

• Strong provenance supports the recorded origin: still check the real-world claim.

• Multiple independent signals indicate generation or manipulation: explain each signal and remaining uncertainty.

For a fuller process, use the guide to verify an image’s source and authenticity and the explanation of how AI image detectors work.

Avoid common interpretation mistakes

Treating a clue as proof

An unusual hand, smooth face or broken word can result from generation, editing, optics or compression. Require a pattern of independent evidence.

Ignoring “real image, false context”

Reverse search may show that every pixel is authentic but the photograph is from another year or country. Authenticity and contextual truth are separate questions.

Testing only a screenshot

A screenshot adds a new processing history. Whenever possible, test the highest-quality original as well and document which version produced the result.

Assuming metadata absence means AI

Most metadata is ordinary, editable information rather than a cryptographic guarantee. Its absence cannot identify the tool that created an image.

Asking a detector to settle a dispute

Detectors can generate false positives—real images labelled AI—and false negatives—AI images labelled real. See the detailed guide to AI detector accuracy and false positives before using a score in moderation, journalism or an investigation.

When to escalate the image

Seek a qualified digital-forensics professional when an image may affect legal proceedings, employment, public safety, insurance, identity verification or a person’s reputation. Preserve the original file and chain of custody. Do not repeatedly re-save it, strip metadata or mark over the only copy.

A professional may compare camera sensor patterns, compression history, related source files and file-system evidence. Even then, conclusions should state the methods, limits and alternative explanations.

Final Summary

The safest way to tell if an image is AI generated is to combine visual inspection with contextual and technical checks. Anatomy, lettering, reflections, repeated details and perspective can reveal reasons for concern, but none is a universal signature. Start with the claim and publisher, preserve the best copy, search for earlier appearances, examine metadata or Content Credentials, and add a detector result only as another signal.

Your conclusion should be narrower than your evidence. “Suspicious and unverified” is often more accurate than “fake,” while “no anomaly found” does not prove authenticity. For more practice, browse the AI image detection guides and add original, labelled examples from the site’s own testing before publication.