Try it Out

You can test out the API without code by going to the FastAPI link with your web browser: https://ai-image-detect.undetectable.ai/docs

Authentication

Undetectable.AI uses API keys to allow access to the API. You can get your API key at the top of the page in our developer portal.

UD expects for the API key to be included in all API requests to the server in a request body that looks like the following:

key: YOUR API KEY GOES HERE

AI Image Detector

Detect (3-Step Process)

The AI Image Detection workflow consists of the following steps:

• Obtain a Pre-signed Upload URL
• Upload the Image
• Submit the Image for Detection

1. Obtain a Pre-signed Upload URL

Begin by requesting a pre-signed URL from the API. This URL allows you to securely upload your image file to the storage server.

Supported File Formats: JPG, JPEG, PNG, WebP, JFIF, HEIC, HEIF, AVIF, BMP, TIFF, TIF , GIF , SVG, PDF

Note: It is necessary to remove spaces from the image filename when requesting a pre-signed URL.

PDF note: For PDF files, only the first image will be detected.

Example Request

curl -X GET 'https://ai-image-detect.undetectable.ai/get-presigned-url?file_name=example.jpg' \
--header 'apikey: YOUR API KEY GOES HERE'

Example Response

{
  "status": "success",
  "presigned_url": "https://nyc3.digitaloceanspaces.com/ai-image-detector-dev/uploads/581d47c7-3ef4-42af-88d9-6dab6bf69389_20250611-121955_example.jpg...",
  "file_path": "uploads/example.jpg"
}

2. Upload the Image

Use the provided presigned_url to upload your image via a PUT request. Ensure the correct content type is set according to your image format.

Example Request

curl -X PUT 'https://nyc3.digitaloceanspaces.com/ai-image-detector-dev/uploads/581d47c7-3ef4-42af-88d9-6dab6bf69389_20250611-121955_example.jpg...' \
  --header 'Content-Type: image/jpeg' \
  --header 'x-amz-acl: private' \
  --data-binary '@example.jpg' # Attachment

Set the Content-Type header to match your file extension exactly:

• image/jpeg: jpg, jpeg, jfif
• image/png: png
• image/webp: webp
• image/heic: heic
• image/heif: heif
• image/avif: avif
• image/bmp: bmp
• image/tiff: tiff, tif
• image/gif: gif
• image/svg+xml: svg
• application/pdf: pdf

# PNG example
curl -X PUT '<PRESIGNED_URL_FOR_example.png>' \
  --header 'Content-Type: image/png' \
  --header 'x-amz-acl: private' \
  --data-binary '@example.png'

# PDF example
curl -X PUT '<PRESIGNED_URL_FOR_example.pdf>' \
  --header 'Content-Type: application/pdf' \
  --header 'x-amz-acl: private' \
  --data-binary '@example.pdf'

# SVG example
curl -X PUT '<PRESIGNED_URL_FOR_example.svg>' \
  --header 'Content-Type: image/svg+xml' \
  --header 'x-amz-acl: private' \
  --data-binary '@example.svg'

Common mistakes to avoid:

• Do not use image/jpg (incorrect). Use image/jpeg.
• Do not mismatch file and header (e.g., .png file with image/jpeg).
• Do not change the extension without updating the header (or vice versa).
• Do not include spaces in filenames when requesting/uploading.

Note: It is necessary to remove spaces from the image filename when uploading the image.

Ensure that the file format remains consistent during the upload process. A successful upload will return a status code of 200.

File Size Limits:

• Minimum file size: 1KB
• Maximum file size: 10MB

3. Submit Image for AI Detection

After uploading, submit the image for AI detection by referencing the file_path from the previous step.

For PDF uploads, only the first image will be analyzed/detected.

Example Request

curl -X 'POST' \
  'https://ai-image-detect.undetectable.ai/detect' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "key": "YOUR-API-KEY-GOES-HERE",
  "url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/<FILE_PATH>",
  "generate_preview": false
}'

The FILE_PATH refers to the path obtained from the response in the initial step, "Obtain a Pre-signed Upload URL".

Optional Parameters:

• generate_preview: Set to true to generate a preview URL for the image (default: false)
• document_type: Type of document (default: Image)
• email: Email address for processing
• generate_analysis_details: Set to false to skip generating detailed analysis result (default: True)

Example Response

{
    "id": "77565038-9e3d-4e6a-8c80-e20785be5ee9",
    "status": "pending"
}

The response includes a unique image ID for tracking the detection status.

Bulk Upload (ZIP)

Submit multiple images for AI detection in a single request by uploading a ZIP file. The workflow mirrors the single-image flow:

• Obtain a Pre-signed Upload URL (for the ZIP file)
• Upload the ZIP
• Submit the ZIP for Bulk Detection
• Query Bulk Upload Results

1. Obtain a Pre-signed Upload URL (ZIP)

Request a pre-signed URL for your ZIP file. Use the same get-presigned-url endpoint with a .zip filename.

Example Request

curl -X GET 'https://ai-image-detect.undetectable.ai/get-presigned-url?file_name=images.zip' \
--header 'apikey: YOUR API KEY GOES HERE'

2. Upload the ZIP

Use the provided presigned_url to upload your ZIP via a PUT request.

Example Request

curl -X PUT '<PRESIGNED_URL_FOR_images.zip>' \
  --header 'Content-Type: application/zip' \
  --header 'x-amz-acl: private' \
  --data-binary '@images.zip'

ZIP Limits:

• Maximum ZIP size: 100MB
• Maximum images per bulk: 50
• Per-image limits: minimum 1KB, maximum 10MB

Supported formats inside ZIP: JPG, JPEG, PNG, WebP, JFIF, HEIC, HEIF, AVIF, BMP, TIFF, TIF, GIF, SVG

Note: PDF files inside the ZIP are not supported and will be skipped. SVG files are converted to PNG before detection.

3. Submit ZIP for Bulk Detection

Example Request

curl -X 'POST' \
  'https://ai-image-detect.undetectable.ai/bulk-upload' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "key": "YOUR-API-KEY-GOES-HERE",
  "url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/<FILE_PATH>",
  "generate_preview": false,
  "generate_analysis_details": false,
  "model": "generic"
}'

The FILE_PATH refers to the path from the presigned URL response (e.g., uploads/images.zip).

Optional Parameters:

• generate_preview: Set to true to generate preview URLs for images (default: false)
• generate_analysis_details: Set to true to generate detailed analysis results (default: false)
• model: Model domain - generic, sheerid, or instance_id/model format

Example Response

{
    "id": "77565038-9e3d-4e6a-8c80-e20785be5ee9",
    "status": "pending",
    "expected_count": 12
}

4. Query Bulk Upload Results

Use the /bulk-upload/query endpoint (not /query) to retrieve results for a bulk upload. Poll until status is done.

Example Request

curl -X 'POST' \
  'https://ai-image-detect.undetectable.ai/bulk-upload/query' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "id": "77565038-9e3d-4e6a-8c80-e20785be5ee9"
}'

Example Response

{
    "id": "77565038-9e3d-4e6a-8c80-e20785be5ee9",
    "status": "done",
    "result": 90.23,
    "items": [
        {
            "filename": "image1.jpg",
            "result": 90.23,
            "final_result": "AI Generated",
            "confidence": 90.23,
            "detection_step": 3,
            "metadata": ["..."],
            "ocr": ["OCR did not detect AI", 0.0],
            "ml_model": ["AI Generated", 90.23],
            "error": null
        }
    ],
    "skipped": [
        {
            "filename": "document.pdf",
            "reason": "PDF not supported in bulk"
        }
    ]
}

Response fields:

• items: Per-image results. Each item has filename, result, final_result, confidence, detection_step, metadata, ocr, ml_model, and error (if detection failed).
• skipped: Files that were not processed (invalid path, unsupported type, too small, too large, etc.).

Billing: Credits are deducted only for images that are successfully queued for detection. Failed SVG conversions and skipped files are not billed.

Query Detection Status and Results

To check the status and retrieve the results, use the /query endpoint with the image ID.

Example Request

curl -X 'POST' \
  'https://ai-image-detect.undetectable.ai/query' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "id": "IMAGE-ID-GOES-HERE"
}'

Example Response

{
    "id": "00fee5ff-a55b-42fb-b7c7-d14f05ae0769",
    "status": "done",
    "result": 90.2371538185235,
    "result_details": {
        "is_valid": true,
        "detection_step": 3,
        "final_result": "AI Generated",
        "metadata": [
            "No Information Detected for Real/AI",
            "Could not find anything from ExifTool and Pillow metadata"
        ],
        "metadata_basic_source": "null",
        "ocr": [
            "OCR did not detect AI",
            0.0
        ],
        "ml_model": [
            "AI Generated",
            90.2371538185235
        ],
        "confidence": 90.2371538185235,
        "heatmap_status": "ready",
        "heatmap_url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/uploads/....",
        "analysis_result_status":"pending",
        "analysis_result":null
    },
    "preview_url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/previews/..."
}

Notes:

3. Heatmap generation is asynchronous. Initially, heatmap_status will be pending and heatmap_url may be absent or null. When ready, heatmap_status becomes ready and heatmap_url is populated.
4. Analysis result detail generation (unless turned off in detect query) is asynchronous. Initially, analysis_result_status will be pending and analysis_result may be absent or null. When ready, analysis_result_status becomes ready and analysis_result is populated.

Tip: You have poll again even after initial result settles to get the pending updates like heatmap & analysis_results

Example Response when anaylsis result

{
    "id": "00fee5ff-a55b-42fb-b7c7-d14f05ae0769",
    "status": "done",
    "result": 90.2371538185235,
    "result_details": {
        "is_valid": true,
        "detection_step": 3,
        "final_result": "AI Generated",
        "confidence": 90.2371538185235,
        "heatmap_status": "ready",
        "heatmap_url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/uploads/....",
        "analysis_results_status": "done",
        "analysis_results": {
            "imageTag": [
                "person",
                "portrait",
                "outdoor",
                "vineyard",
                "smiling"
            ],
            "agreement": "strong",
            "confidence": 92,
            "keyIndicators": [
                "Unnaturally smooth skin texture",
                "Consistent lighting anomalies"
            ],
            "detailedReasoning": "The image shows clear signs of AI generation with unnaturally smooth textures and consistent lighting patterns not typical of real photography.",
            "visualPatterns": [
                "Uniform noise pattern typical of diffusion models"
            ],
            "recommendations": [
                "Cross-reference with original source if available",
                "Check for metadata inconsistencies",
                "Compare with known AI generation patterns"
            ]
        }
    },
    "preview_url": "https://ai-image-detector-prod.nyc3.digitaloceanspaces.com/previews/..."
}

Result Details

• is_valid: Indicates if the image file is valid (true/false)
• detection_step: Indicates the stage at which detection was completed.
• 1: Only metadata is returned.
• 2: Returns metadata and ocr results.
• 3: Returns metadata, ocr, and ml_model results.
• final_result: The overall determination (e.g., "AI Generated", "Real", "Digitally Edited, "AI Edited").
• confidence: The confidence score of the detection.
• metadata: Information extracted from image metadata using ExifTool and Pillow.
• metadata_basic_source: This information may indicate whether the image was captured using a specific mobile camera model, generated by an AI tool, or modified using photo-editing software.
• ocr: Results from Optical Character Recognition analysis for watermark detection.
• ml_model: Results from the machine learning model.
• preview_url: URL to the preview image (if generate_preview was set to true).
• heatmap_status: Status of heatmap generation (pending or ready).
• heatmap_url: URL to the heatmap image, available when heatmap_status is ready.
• analysis_result_status: Status of analysis result generation (pending or ready When you send generate_analysis_details=true in detect request), (null when you don't send generate_analysis_details or send it as generate_analysis_details=false ) in the /detect request.
• analysis_results: Explained below

Analysis Result Explanation

The analysis_result_status field will be one of:

• pending: Processing is queued
• analyzing: AI detection is in progress
• done: Results are available
• failed: Processing failed

• analysis_result When not ready, it will be null, otherwise will have structure like this:
• 1. agreement: type: string, values: "strong" | "moderate" | "weak" | "disagreement"
• 2. imageTags string[] (array of 5 tags for this image)
• 3. confidence: number (0-100)
• 4. keyIndicators: string[] (array of specific, concrete audio anomalies and technical artifacts that directly suggest AI generation - focus on discrete, identifiable issues like "unnatural breath patterns", "synthetic voice characteristics", "inconsistent speech rhythm", "artificial prosody", "compression artifacts affecting voice", etc.),
• 5. detailedReasoning: string (comprehensive explanation 2-4 sentences explaining why the score is accurate),
• 6. audioPatterns: string[] (array of broader stylistic and compositional patterns that characterize the audio - focus on overall trends like "synthetic voice generation signatures", "unnatural speech cadence", "artificial prosody patterns", "inconsistent background noise integration", "audio compression characteristics", etc.),
• 7. recommendations: string[] (3-5 actionable recommendations for understanding or verifying the audio)

Check User Credits

This endpoint accepts the users apikey via the header. And returns users credit details.

Example Request

curl -X 'GET' \
  'https://ai-image-detect.undetectable.ai/check-user-credits' \
  -H 'apikey: YOUR API KEY GOES HERE' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \

Example Response

{
    "baseCredits": 10000,
    "boostCredits": 1000,
    "credits": 11000
}

Note: For external integrations, only the credits field will be populated.

Health Check

Check the health status of the API server.

Example Request

curl -X 'GET' \
  'https://ai-image-detect.undetectable.ai/health' \
  -H 'accept: application/json'

Example Response

{
    "status": "healthy"
}

Errors

The generic error codes we use conform to the REST standard:

Common Issues and Solutions

Authentication Issues

"User verification failed" (403)

• Cause: Invalid or expired API key
• Solution:

1. Verify your API key is correct
2. Check if your API key is active in your account
3. Try regenerating your API key

"Not enough credits" (403)

• Cause: Insufficient credits for image processing
• Solution:

1. Check your remaining credits using /check-user-credits
2. Purchase additional credits if needed

Input Validation Issues

"Input URL cannot be empty" (400)

• Cause: Empty or invalid URL submitted
• Solution:

1. Ensure your url input is not empty
2. Remove any leading/trailing whitespace in image names
3. Check if url encoding is correct

"Input email is empty" (400)

• Cause: Missing email for URL processing
• Solution:

1. Provide a valid email address when submitting URLs
2. Check email format is correct

"Unsupported image type" (400)

• Cause: File format not supported
• Solution:

1. Convert image to supported format (JPG, PNG, WebP, HEIC, HEIF, AVIF, BMP, TIFF, GIF, SVG, PDF)
2. Check file extension is correct

"File size is too small" (400)

• Cause: Image file is below minimum size requirement
• Solution:

1. Use a larger image file (minimum 1KB)
2. Check if image was corrupted during upload

"File size exceeds limit" (400)

• Cause: Image file is too large
• Solution:

1. Compress or resize image (maximum 50MB)
2. Use a different image format

"Invalid file type" (400)

• Cause: File type validation failed
• Solution:

1. Ensure file is a valid image format
2. Check file is not corrupted
3. Verify MIME type matches file extension

Processing Issues

Image Status "failed"

• Cause: Processing failed for various reasons
• Solution:

1. Verify URL is in a supported format
2. Check image file is valid and not corrupted
3. Ensure image meets size requirements
4. Contact support if issue persists

"User not found"

• Cause: Invalid user ID
• Solution:

1. Verify user ID is correct
2. Ensure user account is active
3. Re-authenticate if needed

"File metadata could not be fetched" (500)

• Cause: Unable to access uploaded file
• Solution:

1. Verify file was uploaded successfully
2. Check file URL is accessible
3. Try re-uploading the file

Bulk Upload Issues

"URL must point to a ZIP file" (400)

• Cause: The URL provided to /bulk-upload does not point to a ZIP file
• Solution:

1. Use get-presigned-url?file_name=images.zip (or another .zip filename)
2. Upload a valid ZIP file to the presigned URL
3. Ensure the URL in the bulk-upload request points to the uploaded ZIP

"ZIP file too large" (400)

• Cause: ZIP exceeds maximum size (100MB)
• Solution:

1. Reduce the number of images or compress them
2. Split into multiple bulk uploads

"Too many files" (400)

• Cause: ZIP contains more than 50 valid images
• Solution:

1. Reduce to 50 or fewer images per ZIP
2. Split into multiple bulk uploads

"No valid images found in ZIP" (400)

• Cause: All files in the ZIP were skipped (unsupported format, too small, invalid path, etc.)
• Solution:

1. Ensure images use supported formats (JPG, PNG, WebP, HEIC, HEIF, AVIF, BMP, TIFF, TIF, GIF, SVG)
2. PDF is not supported in bulk
3. Each image must be at least 1KB and at most 10MB
4. Avoid hidden files (names starting with .) and path traversal (..)

"Document is not a bulk upload (image-zip) result" (400)

• Cause: Using /bulk-upload/query with an ID from single-image /detect
• Solution:

1. Use /query for single-image documents
2. Use /bulk-upload/query only for documents returned by POST /bulk-upload

Environment Variables

• BYPASS_OCR: When set to true, the worker will bypass the OCR watermark detection step and only use metadata and the ML model. Default is false.

Updated on: 13/03/2026