Processing Image Files

Private AI supports scanning images for PII and creating de-identified or redacted copies. Private AI’s supported entity types function across each file type, with localized variants of different PII (Personally Identifiable Information) entities, PHI (Protected Health Information) entities, and PCI (Payment Card Industry) entities being detected. Our Supported Languages and Supported Entity Types page provides a more detailed look.

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If you'd like to try it yourself, please visit our free interactive web demo. No code or account is necessary.

How Images Are Processed

Image files are processed as follows:

Non-text PII such as faces and license plates are detected and de-identified via blurring or black box redaction.
The resulting image is run an OCR system to detect any text present.
The output of the OCR system is passed to a PII detection module.
Any PII found in the previous step is de-identified via blurring or black box redaction.

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Check out our OCR Guide to see the available OCR modes.

Constraints

There is limited support for 8-bit PNG images.

Supported File Types

File Type	Extension	Content Type	Added In
JPEG image	`.jpg`, `.jpeg`	`image/jpg`, `image/jpeg`	3.0.0
TIFF image	`.tif`, `.tiff`	`image/tif`, `image/tiff`	3.0.0
PNG image	`.png`	`image/png`	3.4.0
BMP image	`.bmp`	`image/bmp`, `image/x-ms-bmp`	3.4.0

Support Matrix

	CPU Container	GPU Container	Community API	Professional API	PrivateGPT UI
Supported?	Yes	Yes	Yes	Yes	No

Sample Request

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Please sign up for a free API key to run this code.

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import requests
import base64

file_url = "https://paidocumentation.blob.core.windows.net/$web/sample.jpeg"
file_content = requests.get(file_url).content
file_content_base64 = base64.b64encode(file_content).decode()

url = "https://api.private-ai.com/deid/v3/process/files/base64"

headers = {"Content-Type": "application/json", "x-api-key": "<INSERT API KEY>"}

payload = {
  "file":{
    "data": file_content_base64,
    "content_type": "image/jpeg",
  },
  "entity_detection": {
    "accuracy": "high",
    "return_entity": True
  }
}

response = requests.post(url, json=payload, headers=headers)

Sample Response

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"processed_file": "Base64 Encoded File Content of the Redacted File",
"processed_text":"string",
"entities":"List[Entity]",
"entities_present":true,
"languages_detected":{"lang_1":0.67, "lang_2": 0.74}