How to Detect AI-Generated Images: A Technical Guide

AI image generators have gotten remarkably good. Models like DALL-E, Midjourney, Stable Diffusion, and Flux produce images that are increasingly difficult to distinguish from real photographs at a glance. But "difficult" does not mean "impossible." Every generation method leaves traces, and forensic analysis can reveal them.

This guide walks through the core techniques used to detect AI-generated and manipulated images, from simple metadata checks to advanced signal processing.

Why Detection Matters

The stakes are higher than spotting fake art. AI-generated images are used in disinformation campaigns, fraudulent product listings, fake identity documents, and social engineering attacks. Being able to verify image authenticity is becoming a core skill for journalists, researchers, content moderators, and security professionals.

Technique 1: Metadata Analysis

The simplest check is often the most revealing. Real photographs carry EXIF metadata — camera model, focal length, GPS coordinates, creation date, and software used. AI-generated images typically have no EXIF data at all, or they carry metadata from the generation tool (like "Stable Diffusion" in the software field).

What to look for:

• Missing camera information (no make, model, or lens data)
• Software fields containing AI tool names
• Inconsistent timestamps or creation dates
• Missing or generic GPS data

Limitation: Metadata can be stripped or spoofed, so its absence is not conclusive evidence of AI generation, but its presence (with consistent camera data) is a strong indicator of authenticity.

Technique 2: Error Level Analysis (ELA)

Error Level Analysis works by re-saving a JPEG image at a known compression level and comparing the result to the original. In a genuine, unedited photograph, the error levels should be relatively uniform across the image. Edited or AI-generated regions show different error patterns because they have not been through the same compression history.

How it works:

1. Re-compress the image at a fixed quality (e.g., JPEG 95%)
2. Compute the pixel-by-pixel difference between original and re-compressed versions
3. Amplify the differences to make them visible

What to look for:

• Uniform bright regions in ELA suggest AI generation (consistent "freshness")
• Patchy or inconsistent error levels suggest compositing or inpainting
• Edges and high-contrast areas naturally show higher error — this is normal

Technique 3: FFT Spectral Analysis

Fast Fourier Transform (FFT) analysis converts an image from spatial domain to frequency domain, revealing periodic patterns invisible to the naked eye. AI-generated images often exhibit distinctive spectral signatures:

• Grid artifacts from the generator's internal resolution (e.g., 64x64 latent space upscaled to 512x512)
• Unusual frequency distributions compared to natural photographs
• Missing high-frequency detail that real camera sensors capture

What to look for:

• Bright spots or crosses in the FFT spectrum that indicate repeating patterns
• Unusually smooth or symmetric frequency distributions
• Absence of the natural noise floor present in camera sensor data

Technique 4: Noise Pattern Analysis

Every camera sensor has a unique noise pattern — a combination of shot noise, read noise, and fixed-pattern noise. AI generators do not replicate these sensor-specific noise characteristics. Instead, they produce their own distinctive noise profiles.

Analysis methods:

• Extract the noise residual by subtracting a denoised version from the original
• Analyze the noise distribution — camera noise follows known statistical models
• Check for noise consistency across the image — real photos have uniform sensor noise, while composited images have mismatched noise regions

What to look for:

• Unnaturally uniform noise (AI generators tend to produce smooth noise)
• Noise patterns that do not match any known camera sensor
• Inconsistent noise levels between different regions of the same image

Technique 5: JPEG Ghost Analysis

JPEG ghost analysis exploits the fact that JPEG compression is lossy and cumulative. By re-compressing an image at every quality level (1-100) and measuring the difference, you can detect:

• Double compression — regions that have been through JPEG compression a different number of times (indicating compositing)
• Quality level mismatches — areas saved at different quality settings
• Editing artifacts — regions where tools like Photoshop or AI inpainting have altered the compression history

Technique 6: Color and Statistical Analysis

AI-generated images often have subtle statistical anomalies in their color distributions:

• Color histogram analysis — real photos have natural color distributions shaped by scene lighting; AI images may have unnatural peaks or gaps
• Channel correlation — the RGB channels in natural photos are correlated in predictable ways; AI images may break these correlations
• Benford's Law — the distribution of first digits in pixel values follows Benford's Law for natural images; AI-generated images may deviate

Putting It All Together

No single technique is definitive. The most reliable approach is a multi-method pipeline that combines several analyses:

1. Check metadata first (quick win)
2. Run ELA for overall integrity assessment
3. Apply FFT for spectral anomalies
4. Analyze noise patterns for sensor consistency
5. Check JPEG compression history
6. Review statistical color properties

When multiple techniques agree, confidence is high. When results conflict, further investigation is needed.

Try It Yourself

The BlestLabs Media Forensics tool implements a 9-step forensic analysis pipeline using pure signal processing — no AI models required. Upload any image to get a comprehensive authenticity report covering ELA, FFT spectral analysis, noise patterns, JPEG ghost analysis, metadata inspection, and more.

The tool runs the same techniques described in this article, automated and presented in a clear, visual report. It is free to use and your images are processed server-side with no retention.

Further Reading

• Farid, H. (2022). Digital Image Forensics. MIT Press.
• Verdoliva, L. (2020). "Media Forensics and DeepFakes: An Overview." IEEE Journal of Selected Topics in Signal Processing.
• The Coalition for Content Provenance and Authenticity (C2PA) — c2pa.org