A high-precision AI-generated content (AIGC) detection engine that distinguishes real photos from AI art using CamScanner's advanced verification technology.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install camscanner-image-detect-aigc
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install camscanner-image-detect-aigc using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CamScanner Image Detect AIGC?
The CamScanner Image Detect AIGC skill is a specialized tool within the Openclaw Skills ecosystem designed to combat misinformation and verify digital authenticity. It leverages a sophisticated AIGC-detection engine to identify images produced by popular generative models like Midjourney, Stable Diffusion, and DALL-E.
By integrating this skill, AI agents can provide users with factual certainty regarding whether a visual asset is a genuine photograph or a synthetic creation. This Openclaw Skills component acts as a critical layer of trust for developers building tools that require image verification or content moderation.
CamScanner Image Detect AIGC Use Cases
- Identifying AI-generated artwork in professional or academic contexts.
- Verifying the authenticity of news-related photos to prevent misinformation.
- Distinguishing between human-captured photography and Stable Diffusion or Midjourney outputs.
- Performing bulk audits on image repositories using Openclaw Skills workflows.
How CamScanner Image Detect AIGC Works
- The agent captures the user's image and initiates the Openclaw Skills workflow.
- The image is uploaded to the CamScanner processing server to receive a unique file identifier.
- The validation engine analyzes the image data specifically for AIGC patterns using mode 2.
- The system returns a JSON payload containing a classification code (1-3) and a confidence score.
- The agent interprets the result and presents a clear, human-readable verdict to the user.
CamScanner Image Detect AIGC Setup
To utilize this detection capability within Openclaw Skills, ensure your environment has curl and jq installed for handling API requests and parsing results:
# Check for required dependencies
which curl jq
# Example upload sequence
IN_FILE_ID=$(curl -sS -X POST "https://ai-tools.camscanner.com/v1/tools/upload_file/execute" \
-H "Content-Type: application/octet-stream" \
--data-binary "@/path/to/image.jpg" | jq -r '.tool_result.data.file_id')
CamScanner Image Detect AIGC Data Schema & Taxonomy
This skill processes image data and returns a structured JSON response to help Openclaw Skills agents categorize the content:
| Field | Type | Meaning |
|---|---|---|
| ai_check_result | integer | 1 = real, 2 = suspected AI, 3 = confirmed AI |
| confidence | float | Model confidence score between 0 and 1 |
| result_text | string | Human-readable conclusion (default Chinese) |
| engine | string | Detection engine used (aigcdetection) |
CamScanner Image Detect AIGC Advanced Features
- Real-time multi-stage validation for high-volume image verification tasks.
- Multilingual flexibility through agent-level translation of detection summaries.
- Seamless integration with other Openclaw Skills for automated content moderation pipelines.
- Precision confidence scoring allowing for nuanced authenticity reporting beyond simple binary checks.
SKILL.md
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