Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@image-forensics-mcp Analyze this image for AI generation: /path/to/image.jpg"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
One command — requires uv:
claude mcp add -s user image-forensics -- uvx --from git+https://github.com/surewht/image-forensics-mcp image-forensics-mcpThat's it. Restart Claude Code and the tools are available.
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-mcpAdd to ~/.claude.json:
{
"mcpServers": {
"image-forensics": {
"type": "stdio",
"command": "uvx",
"args": ["--from", "git+https://github.com/surewht/image-forensics-mcp", "image-forensics-mcp"]
}
}
}Related MCP server: Image MCP Server
Tool | What it does |
| Extracts the noise pattern by subtracting a blurred version. AI images have unnaturally uniform noise. Returns visualization + statistics. |
| Multi-scale ELA — resaves at multiple JPEG qualities and analyzes error patterns. Detects splicing and compression inconsistencies. |
| 2D FFT spectrum analysis. Detects GAN grid artifacts and unusual frequency distributions. |
| Power spectral density curve fitting (1/f^β). Based on CVPR 2025 research. Natural images follow characteristic decay; AI images may deviate. |
| RGB channel correlation, gradient correlation, entropy, and saturation analysis. Based on CVPR 2025 "Secret Lies in Color" research. |
| EXIF/metadata inspection for AI tool signatures (Stable Diffusion, DALL-E, Midjourney, etc.) + AI resolution fingerprinting. |
| Runs all 6 analyses, saves visualizations to |
In Claude Code, just ask:
Analyze this image for AI generation: /path/to/image.jpgOr use individual tools:
Run a noise map analysis on /path/to/image.png
Check the metadata of /path/to/image.webpThe 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 |
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 |
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
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.
Python >= 3.10
uv (for uvx install method)
Dependencies (installed automatically): mcp[cli], Pillow, numpy, scipy
MIT
Analyze color channel correlations, saturation, and entropy for AI detection.
Based on CVPR 2025 "Secret Lies in Color" — AI-generated images show unusual RGB channel correlations, gradient patterns, and saturation distributions compared to natural photographs.
Args: image_path: Absolute path to the image file.
Inputs: image_path (required). Returns: result
Perform multi-scale Error Level Analysis (ELA) on an image.
Resaves at multiple JPEG quality levels and analyzes the error patterns. Includes consistency analysis across scales for better AI detection.
Args: image_path: Absolute path to the image file. quality: Primary JPEG quality for visualization (1-100). Default 90.
Inputs: image_path (required), quality. Returns: result
Perform FFT frequency analysis on an image.
Analyzes the frequency spectrum for artifacts common in AI-generated images, including grid patterns, spectral peaks, and unusual energy distribution.
Args: image_path: Absolute path to the image file.
Inputs: image_path (required). Returns: result
Run all forensic analyses and produce a comprehensive report with overall verdict.
Combines noise map, ELA, frequency analysis, spectral decay, color analysis, and metadata check. Saves visualization images to /tmp/image-forensics/ and returns text-only results to avoid output overflow.
Args: image_path: Absolute path to the image file.
Inputs: image_path (required). Returns: result
Check image metadata/EXIF for AI generation signatures and resolution fingerprints.
Looks for known AI tool signatures, unusual metadata patterns, missing camera information, and AI-typical output resolutions.
Args: image_path: Absolute path to the image file.
Inputs: image_path (required). Returns: result
Extract the noise pattern from an image to help detect AI generation.
Subtracts a Gaussian-blurred version from the original to isolate noise. AI-generated images tend to have unnaturally uniform or structured noise.
Args: image_path: Absolute path to the image file. blur_sigma: Gaussian blur sigma (higher = more smoothing). Default 3.0.
Inputs: image_path (required), blur_sigma. Returns: result
Analyze the power spectral density decay curve of an image.
Natural images follow a characteristic 1/f^beta power law. AI-generated images (especially from diffusion models) often show different decay slopes and anomalies in specific frequency bands. Based on CVPR 2025 research.
Args: image_path: Absolute path to the image file.
Inputs: image_path (required). Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 7 tools
Each tool targets a distinct forensic method (noise, ELA, FFT, spectral decay, color, metadata). However, frequency_analysis and spectral_decay_analysis both operate in the frequency domain and could be confused by an agent unfamiliar with the specific techniques. The descriptions clarify the differences, so overlap is limited.
Six of seven tools follow a consistent noun_analysis or noun_check pattern (e.g., noise_map, frequency_analysis, metadata_check). The outlier is full_forensic_report, which is a different type (aggregate report) but its name is still descriptive. Minor deviation from a strict pattern.
Seven tools is well-scoped for an image forensics server. Each tool provides a distinct analysis technique, and the inclusion of a full report tool adds convenience without redundancy. No tool feels unnecessary or missing.
The surface covers multiple orthogonal forensic techniques: noise, compression artifacts, frequency domain, spectral decay, color, metadata, and a comprehensive aggregator. This is complete for the stated purpose of AI image detection; no obvious gaps exist.
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