auth-securityai-ml

Proofly (Deepfake Detection)

by prooflie

Enable advanced deepfake detection in images using Proofly API. Get real/fake probability scores with cutting-edge deepf

Enables deepfake detection in images through Proofly API integration, providing detailed analysis results including real/fake probability scores and individual model results for each detected face.

github stars

1

Remote — zero setup requiredMultiple deployment options (SSE streaming or HTTP)Per-face detailed analysis

best for

  • / Content moderation and verification
  • / Security teams validating user-submitted images
  • / Media organizations fact-checking photos
  • / Developers building authentication systems

capabilities

  • / Analyze images from URLs for deepfake detection
  • / Process base64-encoded images for face authenticity
  • / Get detailed face-by-face analysis results
  • / Check analysis session status and progress
  • / Generate real/fake probability scores per face

what it does

Detects deepfakes and face swaps in images by analyzing faces and providing probability scores for authenticity. Works with both image URLs and base64-encoded images.

about

Proofly (Deepfake Detection) is an official MCP server published by prooflie that provides AI assistants with tools and capabilities via the Model Context Protocol. Enable advanced deepfake detection in images using Proofly API. Get real/fake probability scores with cutting-edge deepf It is categorized under auth security, ai ml. This server exposes 4 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Proofly (Deepfake Detection) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Proofly (Deepfake Detection) is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Proofly MCP Integration

Install and just write 'proofly it' URL to content or analyze it URL to content for deepfake face swap analysis.

  1. For clients that connect to MCP servers using a URL (e.g., Cursor, Cascade/Windsurf)

Add one of the following configurations to your MCP client (e.g., in mcp_config.json):

A. Streaming (SSE - Recommended where supported):

{
  "proofly": {
    "serverUrl": "https://mcp.proofly.ai/sse",
    "supportedMethods": [
      "analyze-image",
      "analyze",
      "get-face-details",
      "check-session-status"
    ],
    "auth": { "type": "none" } // Or your specific auth if Proofly API https:/get.proofly.ai requires it
  }
}

B. Standard HTTP (Non-streaming):

{
  "proofly": {
    "serverUrl": "https://mcp.proofly.ai/mcp",
    "supportedMethods": [
      "analyze-image",
      "analyze",
      "get-face-details",
      "check-session-status"
    ],
    "auth": { "type": "none" } // Or your specific auth if Proofly API https:/get.proofly.ai requires it
  }
}
  1. For clients that can execute a local command for an MCP server (e.g., Claude Desktop)

Claude Desktop:

  1. Run: npx proofly-mcp@latest
  2. Add to your Claude Desktop config file (e.g., claude_desktop_config.json)
{
  "mcpServers": {
    "proofly": {
      "command": "npx",
      "args": [
        "-y", // The -y flag might be specific to your npm/npx version or aliasing for auto-confirmation.
        "proofly-mcp@latest"
      ],
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}

Alternatively, if you have proofly-mcp installed globally (npm install -g proofly-mcp), you can use:

{
  "mcpServers": {
    "proofly": {
      "command": "proofly-mcp",
      "args": [],
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}

Other command-capable MCP Clients:

If your MCP client can launch a local command, configure it to run proofly-mcp. Conceptual example (actual config varies by client):

{
  "mcpServers": {
    "proofly": {
      "type": "command",
      "command": "proofly-mcp",
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}

Environment Variables for proofly-mcp CLI (Optional)

  • PROOFLY_API_KEY: Your Proofly API key. The proofly-mcp CLI will use this API key if the variable is set when communicating with Proofly API https://get.proofly.ai.

Available MCP Methods

analyze

Analyzes an image from a URL for deepfake detection.

analyze-image

Analyzes an image provided as a base64 string for deepfake detection.

check-session-status

Checks the status of a deepfake analysis session.

get-face-details

Gets detailed information about a specific face detected in an image analysis session.

FAQ

What is the Proofly (Deepfake Detection) MCP server?
Proofly (Deepfake Detection) is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Proofly (Deepfake Detection)?
This profile displays 33 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.533 reviews
  • Pratham Ware· Dec 24, 2024

    We wired Proofly (Deepfake Detection) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Layla Menon· Dec 24, 2024

    Strong directory entry: Proofly (Deepfake Detection) surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Yusuf Jackson· Dec 16, 2024

    According to our notes, Proofly (Deepfake Detection) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Yash Thakker· Nov 15, 2024

    Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Yuki Park· Nov 15, 2024

    I recommend Proofly (Deepfake Detection) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Zara Dixit· Nov 11, 2024

    We evaluated Proofly (Deepfake Detection) against two servers with overlapping tools; this profile had the clearer scope statement.

  • Evelyn Gupta· Oct 26, 2024

    Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Dhruvi Jain· Oct 6, 2024

    Useful MCP listing: Proofly (Deepfake Detection) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Emma Jackson· Oct 6, 2024

    Proofly (Deepfake Detection) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Yuki Farah· Oct 2, 2024

    Proofly (Deepfake Detection) has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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