How to Spot an AI-Generated Image (and Why It Is Getting Harder)

Why this is worth learning

A few years ago, AI images were easy to laugh off. Hands had six fingers, text was gibberish, and faces had a waxy sheen you could spot from across the room. That era is ending. Modern image generators built on diffusion models produce smoother, more photographic results, and many of the old giveaways have quietly disappeared.

So this is not a checklist that guarantees an answer. It is a set of habits. The goal is to slow down, look closely, and treat a single suspicious detail as a reason to investigate further rather than as proof. With that honest framing in mind, here is what still tends to leak.

The classic visual tells

Hands and fingers remain a weak spot, even as they improve. The number of plausible hand positions is enormous, so models still produce extra digits, fused fingers, or thumbs at odd angles. Look at where hands grip objects, that is where errors cluster.

Text inside an image used to be a reliable giveaway, with garbled letters on signs and packaging. Newer models render short text far better, but longer passages, small print, and logos still tend to drift into nonsense or near-misses on closer inspection.

Eyes and teeth are worth a second look. Irises can be mismatched in shape or size, reflections in the two eyes may not agree, and teeth sometimes blur into an unnaturally even row. Faces can also look slightly too symmetrical or too poreless, with skin that reads as smooth plastic rather than living tissue.

Backgrounds, lighting, and physics

The subject of an AI image often looks convincing while the background quietly falls apart. Scan the edges: railings that bend into nothing, repeated patterns in a crowd, text on distant signs that dissolves, or objects that merge into each other where they overlap.

Lighting and shadows are a deeper test because they obey physics that models only approximate. Check whether every shadow points away from the same light source, whether shiny surfaces reflect what should actually be in front of them, and whether a mirror or a pair of sunglasses shows a coherent scene. Reflections that do not match the rest of the frame are a strong clue.

Finally, watch for over-smooth, repetitive textures. Foliage, gravel, hair, fabric, and brickwork in the real world carry irregularity. When a texture looks airbrushed or tiles itself in a faint pattern, that uniformity is a sign of synthesis.

Why detection keeps getting harder

The uncomfortable truth is that visual tells are a moving target. Each new generation of models is trained, in part, to eliminate the artifacts people learned to spot. Diffusion-based generators in particular produce noise profiles that closely mimic real camera sensors, which makes them harder to flag than the older GAN-based images.

Automated AI detectors exist, but treat their verdicts with caution. Independent testing shows their accuracy varies widely by model and image type, and it drops sharply once an image is resized, filtered, screenshotted, or re-saved by a social platform, which is exactly what happens to almost everything you see online. A confident percentage from a detector is an estimate, not a fact.

Because of this arms race, the smart move is to lean less on pixel-hunting and more on context. Where did the image come from? Does a reverse image search show an original source or earlier appearances? Does any reputable outlet corroborate it? Provenance often beats forensics.

Provenance: the C2PA and Content Credentials approach

Rather than trying to detect fakes after the fact, an industry effort aims to attach a verifiable history to media at the moment it is created or edited. The standard is run by the Coalition for Content Provenance and Authenticity, or C2PA, whose steering committee includes Adobe, Amazon, BBC, Google, Meta, Microsoft, OpenAI, Sony, Publicis Groupe, and Truepic.

The consumer-facing label is called Content Credentials, often described as a nutrition label for digital content. Technically it is a cryptographically signed manifest embedded in a file that can record what device or software made the image, whether AI was involved, and what edits followed. If the file is altered after signing, the tampering becomes detectable.

It is important to be precise about what this does and does not do. C2PA asserts positive provenance; it does not classify an image as real or fake, and credentials can be stripped when a file is copied or re-uploaded. Adoption is growing across cameras, phones, and platforms, but coverage is far from universal. Provenance is a promising layer of trust, not a finished solution.

Where this touches ecommerce

If you sell online, two practical lessons follow. First, your buyers are getting more skeptical of images that look too perfect, so honest, accurate product photos build more trust than glossy fantasy renders. Show the real item, its real texture, and its real scale.

Second, there is a clear line between deception and legitimate editing. Cleaning up a cluttered background, placing a product on a clean white backdrop, or squaring up a frame for a marketplace listing does not misrepresent the product itself. That is the spirit behind Renderivo: tidy, marketplace-ready images of the real thing, not invented scenes that mislead a shopper about what arrives in the box.