Image Forensics MCP Server

An MCP (Model Context Protocol) server that gives AI assistants forensic analysis capabilities to help detect AI-generated images. Uses noise maps, Error Level Analysis, FFT frequency analysis, spectral decay analysis, color channel analysis, and metadata inspection.

Install

One command — requires uv:

claude mcp add -s user image-forensics -- uvx --from git+https://github.com/surewht/image-forensics-mcp image-forensics-mcp

That's it. Restart Claude Code and the tools are available.

Alternative: install from local clone

git clone https://github.com/surewht/image-forensics-mcp.git
cd image-forensics-mcp
claude mcp add -s user image-forensics -- uvx --from . image-forensics-mcp

Manual config

Add to ~/.claude.json:

{
  "mcpServers": {
    "image-forensics": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "git+https://github.com/surewht/image-forensics-mcp", "image-forensics-mcp"]
    }
  }
}

Tools

Tool	What it does
noise_map	Extracts the noise pattern by subtracting a blurred version. AI images have unnaturally uniform noise. Returns visualization + statistics.
error_level_analysis	Multi-scale ELA — resaves at multiple JPEG qualities and analyzes error patterns. Detects splicing and compression inconsistencies.
frequency_analysis	2D FFT spectrum analysis. Detects GAN grid artifacts and unusual frequency distributions.
spectral_decay_analysis	Power spectral density curve fitting (1/f^β). Based on CVPR 2025 research. Natural images follow characteristic decay; AI images may deviate.
color_analysis	RGB channel correlation, gradient correlation, entropy, and saturation analysis. Based on CVPR 2025 "Secret Lies in Color" research.
metadata_check	EXIF/metadata inspection for AI tool signatures (Stable Diffusion, DALL-E, Midjourney, etc.) + AI resolution fingerprinting.
full_forensic_report	Runs all 6 analyses, saves visualizations to /tmp/image-forensics/, returns a categorized verdict.

Usage

In Claude Code, just ask:

Analyze this image for AI generation: /path/to/image.jpg

Or use individual tools:

Run a noise map analysis on /path/to/image.png
Check the metadata of /path/to/image.webp

How the verdict works

The full_forensic_report categorizes findings into three tiers:

Tier	What triggers it	Example
Definitive	AI tool signatures in metadata (SD parameters, generation prompts)	Stable Diffusion PNG with parameters chunk
Strong	Forensic anomalies rarely seen in real photos (uniform noise, GAN artifacts, spectral anomalies, extreme saturation)	GAN periodic patterns in FFT spectrum
Weak/Ambiguous	Indicators also caused by social media processing (no EXIF, AI-typical resolution, uniform ELA)	Facebook-resized 768x768 JPEG with stripped metadata

Verdict scale

• AI-GENERATED — Definitive metadata proof found
• LIKELY AI-GENERATED — 3+ strong forensic anomalies
• POSSIBLY AI-GENERATED — 1-2 strong anomalies
• INCONCLUSIVE — Only weak/ambiguous indicators
• LIKELY AUTHENTIC — Minimal indicators, consistent with normal processing
• NO INDICATORS — Clean across all analyses

Limitations

Modern AI generators (Flux, DALL-E 3, Midjourney v6+) produce images that are nearly indistinguishable from real photos at the pixel level. This tool works best when:

• Metadata is intact — SD parameters, generation prompts = definitive proof
• Images show GAN artifacts — periodic patterns in FFT = strong signal
• Images haven't been re-compressed — social media platforms strip metadata and resize to AI-typical dimensions, creating ambiguity

For ambiguous cases, visual inspection (teeth, hands, text, reflections, lighting inconsistencies) combined with these forensic tools gives the best results.

Requirements

• Python >= 3.10
• uv (for uvx install method)

Dependencies (installed automatically): mcp[cli], Pillow, numpy, scipy

License

MIT