How AI Image Detection Actually Works in 2026

Generative models like Midjourney v6, SDXL, Flux, DALL·E 3 and Gemini Image now produce photographs that humans struggle to identify. Modern detectors use a stack of complementary techniques rather than a single classifier.

1. Vision foundation models

A large pre-trained vision model (think CLIP-style or a modern ViT) is fine-tuned on millions of paired real and AI-generated images. It learns subtle texture and frequency-domain features that are hard to articulate but consistent across generators.

2. Frequency-domain artifacts

Diffusion models leave characteristic signatures in the Fourier spectrum — periodic noise patterns at specific frequencies that real camera sensors do not produce. We compute the FFT of the image and look for these telltale peaks.

3. Heuristic forensic checks

• Reflection consistency: do mirrors and water surfaces show the right scene?
• Anatomy: hands, ears, jewelry, and teeth are still failure-prone for diffusion.
• Text rendering: AI-generated text inside images is often warped or misspelled.
• Lighting direction: do shadows agree across all subjects?

4. Identity checks for deepfakes

For face-swap and identity-replaced portraits, we compare facial geometry across frames or against reference identity embeddings. Inconsistent face/neck blending and out-of-distribution skin texture are strong tells.

What you get from TruthLens

A verdict (real, AI-generated, edited, deepfake), a 0–100% confidence score, the suspected generator family, and a human-readable list of the artifacts that drove the decision — so you can audit the verdict, not just trust it.