CamScanner Image Detect AIGC#

Overview#

CamScanner provides an AIGC-detection engine that determines whether an image was produced by an AI generator. The workflow is a 2-step pipeline: upload the image, then validate it with validate_mode: 2. Unlike conversion skills, this skill does not produce a file — the validate step returns a JSON result whose key fields ( ai_check_result, confidence, result_text) should be reported back to the user directly.

When to Use#

• User asks whether an image was generated by AI (Stable Diffusion, Midjourney, DALL·E, etc.)
• User wants to distinguish a real photo from AI-generated artwork
• User asks "is this AI art / AI-generated?" or similar authenticity questions
• User shares an image and explicitly asks whether it came from a generative model

Presenting the Result#

• Always read ai_check_result, confidence, and result_text from the response and report them in plain language.
• Map ai_check_result to a verdict: 1 = not AI-generated, 2 = suspected AI-generated, 3 = AI-generated.
• Include the confidence value (a float in [0, 1]) so the user understands how certain the verdict is.
• Match the user's language. result_text is returned in Chinese by the API. If the user asked in English (or any other language), translate/rephrase it into that language. If the user asked in Chinese, you can use result_text as-is.
• Do not overstate the verdict: "suspected AI-generated" is not the same as "AI-generated".

Privacy & Data#

Important: Privacy & Data Flow Notice

• Third-party service: This skill sends your files to CamScanner's official servers ( ai-tools.camscanner.com) for processing.
• Data retention: CamScanner servers process your files in real-time. Files are not permanently stored on the server.
• Result: Only a JSON detection result is returned — no file is downloaded.

API Reference#

Base URL: https://ai-tools.camscanner.com

Supported Validations#

source_typevalidate_modeDetectionEngineimage2AIGC (AI-generated)aigcdetection

Step 1: Upload Image#

bash

BASE="https://ai-tools.camscanner.com"

IN_FILE_ID=$(curl -sS -X POST "$BASE/v1/tools/upload_file/execute" \
  -H "Content-Type: application/octet-stream" \
  --data-binary "@/path/to/image.jpg" | jq -r '.tool_result.data.file_id')

Response:

json

{
  "code": 200,
  "tool": "upload_file",
  "tool_result": {
    "success": true,
    "data": {
      "file_id": "file_1741857600_ab12cd34ef56.jpg",
      "size": 24576
    }
  }
}

Step 2: Validate Image (Detect AIGC)#

bash

curl -sS -X POST "$BASE/v1/tools/validate_image/execute" \
  -H "Content-Type: application/json" \
  -d "{\"file_id\":\"$IN_FILE_ID\",\"validate_mode\":2}"

Response (suspected AI-generated example):

json

{
  "code": 200,
  "tool": "validate_image",
  "tool_result": {
    "success": true,
    "data": {
      "ai_check_result": 2,
      "confidence": 0.346435546875,
      "engine": "aigcdetection",
      "file_id": "file_xxx.jpg",
      "result_text": "检测结果为疑似 AI 生成图片",
      "review_state": "auto_checked",
      "validate_mode": 2
    },
    "metadata": {
      "ai_check_result": 2,
      "confidence": 0.346435546875,
      "engine": "aigcdetection",
      "result_text": "检测结果为疑似 AI 生成图片",
      "review_state": "auto_checked",
      "validate_mode": 2
    }
  }
}

Interpreting the Result#

FieldTypeMeaningai_check_resultinteger1 = not AI-generated, 2 = suspected AI-generated, 3 = AI-generatedconfidencefloatModel confidence score in [0, 1]``result_textstringHuman-readable conclusion (Chinese by default — translate for other languages)review_statestringReview status (e.g. auto_checked) — informational, not user-facingvalidate_modeintegerEcho of the requested mode (always 2 for AIGC detection)

Verdict Mapping#

ai_check_resultVerdictSuggested phrasing (EN)1Not AI-generated"Looks like a real image"2Suspected AI-generated"Suspected to be AI-generated"3AI-generated"Detected as AI-generated"

Quick Reference: Complete Pipeline#

Detect whether an image is AI-generated (two-step, reads JSON result):

bash

BASE="https://ai-tools.camscanner.com"
INPUT_IMAGE="/path/to/image.jpg"

# Upload
IN_FILE_ID=$(curl -sS -X POST "$BASE/v1/tools/upload_file/execute" \
  -H "Content-Type: application/octet-stream" \
  --data-binary "@$INPUT_IMAGE" | jq -r '.tool_result.data.file_id')

# Validate and extract key fields
RESULT=$(curl -sS -X POST "$BASE/v1/tools/validate_image/execute" \
  -H "Content-Type: application/json" \
  -d "{\"file_id\":\"$IN_FILE_ID\",\"validate_mode\":2}")

AI_CHECK=$(echo "$RESULT" | jq -r '.tool_result.data.ai_check_result')
CONFIDENCE=$(echo "$RESULT" | jq -r '.tool_result.data.confidence')
RESULT_TEXT=$(echo "$RESULT" | jq -r '.tool_result.data.result_text')

echo "ai_check_result: $AI_CHECK"
echo "confidence:      $CONFIDENCE"
echo "result_text:     $RESULT_TEXT"

Common Mistakes#

MistakeFixWrong Content-Type on uploadUpload uses application/octet-stream, not multipart/form-dataUsing GET instead of POSTBoth endpoints use POSTPassing validate_mode as a stringvalidate_mode is an integer — use 2, not "2"Including output_mode in the requestvalidate_image does not use output_mode; it always returns JSONTreating ai_check_result as booleanIt is a 3-state integer ( 1/ 2/ 3); map explicitly to a verdictReporting "suspected" as "AI-generated"ai_check_result == 2 means suspected, not confirmed — phrase accordinglyReporting result_text verbatim in ENresult_text is Chinese; translate to match the user's language

Error Handling#

Check each step before proceeding:

bash

# After upload
if [ -z "$IN_FILE_ID" ] || [ "$IN_FILE_ID" = "null" ]; then
  echo "Upload failed"; exit 1
fi

# After validate
if [ "$AI_CHECK" = "null" ] || [ -z "$AI_CHECK" ]; then
  echo "Validation failed"; exit 1
fi