Oculta datos sensibles de imágenes

Cloud Data Loss Prevention (DLP) puede ocultar texto sensible de una imagen. La API detecta datos sensibles, como la información de identificación personal (IPP) y luego los tapa con un rectángulo opaco.

Por ejemplo, considera la siguiente imagen:

Después de enviar la imagen a Cloud DLP con la configuración predeterminada, la imagen aparece de la siguiente manera:

El método image.redact de la API toma los siguientes elementos como argumentos:

• Por lo menos una imagen codificada en Base64. En este momento, Cloud DLP admite los siguientes tipos de imagen: IMAGE_JPEG, IMAGE_BMP y también IMAGE_PNG.
• Debes incluir un argumento inspectConfig para especificar la información de configuración de detección (InspectConfig), por ejemplo, qué Infotipos debe buscar.
• También puedes incluir un argumento imageRedactionConfigs[], que especifica la información de configuración de ocultamiento (ImageRedactionConfig), como qué color usar para tapar el texto oculto o si solo se debe ocultar texto de ciertos Infotipos o todos los textos.

La API muestra la misma imagen o imágenes que le enviaste, en el mismo formato, pero cualquier texto identificado que contenga información sensible de acuerdo con tus criterios se oculta.

Ejemplo de REST

El siguiente JSON incluye una imagen codificada en Base64 y establece varios parámetros para usar la función de ocultar imágenes de Cloud DLP. La imagen anterior al ocultamiento se muestra a continuación:

La imagen posterior al ocultamiento se muestra aquí:

Consulta la guía de inicio de JSON para obtener más información sobre cómo usar Cloud DLP con JSON.

Entrada de JSON:

POST https://dlp.googleapis.com/v2/projects/[PROJECT_ID]/image:redact?key={YOUR_API_KEY}

{
  "byteItem":{
    "data":"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",
    "type":"IMAGE_JPEG"
  },
  "imageRedactionConfigs":[
    {
      "infoType":{
        "name":"PHONE_NUMBER"
      },
      "redactionColor":{
        "blue":0.1,
        "green":0.1,
        "red":0.8
      }
    },
    {
      "infoType":{
        "name":"PERSON_NAME"
      },
      "redactionColor":{
        "blue":0.1,
        "green":0.8,
        "red":0.1
      }
    }
  ],
  "inspectConfig":{
    "infoTypes":[
      {
        "name":"PHONE_NUMBER"
      },
      {
        "name":"PERSON_NAME"
      }
    ]
  }
}

Resultado de JSON:

{
 "redactedImage": "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"
}

Ejemplos de código

A continuación, hay un código de muestra en varios lenguajes que demuestra cómo usar Cloud DLP para ocultar textos sensibles de una imagen.

Importante: Es necesario configurar un cliente de Cloud DLP para poder ejecutar este código. Para obtener más información sobre cómo instalar y crear un cliente de Cloud DLP, consulta las Bibliotecas cliente de Cloud DLP (no se necesita una biblioteca cliente para enviar JSON a los extremos de REST de Cloud DLP).

Java

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/*
 * Redact sensitive data from an image using the Data Loss Prevention API.
 *
 * @param filePath The path to a local file to inspect. Can be a JPG or PNG image file.
 * @param minLikelihood The minimum likelihood required before redacting a match.
 * @param infoTypes The infoTypes of information to redact.
 * @param outputPath The local path to save the resulting image to.
 * @param projectId The project ID to run the API call under.
 */
private static void redactImage(
    String filePath,
    Likelihood minLikelihood,
    List<InfoType> infoTypes,
    String outputPath,
    String projectId)
    throws Exception {

  // Instantiate the DLP client
  try (DlpServiceClient dlpClient = DlpServiceClient.create()) {
    String mimeType = URLConnection.guessContentTypeFromName(filePath);
    if (mimeType == null) {
      mimeType = MimetypesFileTypeMap.getDefaultFileTypeMap().getContentType(filePath);
    }

    ByteContentItem.BytesType bytesType;
    switch (mimeType) {
      case "image/jpeg":
        bytesType = ByteContentItem.BytesType.IMAGE_JPEG;
        break;
      case "image/bmp":
        bytesType = ByteContentItem.BytesType.IMAGE_BMP;
        break;
      case "image/png":
        bytesType = ByteContentItem.BytesType.IMAGE_PNG;
        break;
      case "image/svg":
        bytesType = ByteContentItem.BytesType.IMAGE_SVG;
        break;
      default:
        bytesType = ByteContentItem.BytesType.BYTES_TYPE_UNSPECIFIED;
        break;
    }

    byte[] data = Files.readAllBytes(Paths.get(filePath));

    InspectConfig inspectConfig =
        InspectConfig.newBuilder()
            .addAllInfoTypes(infoTypes)
            .setMinLikelihood(minLikelihood)
            .build();

    ByteContentItem byteContentItem =
        ByteContentItem.newBuilder()
            .setType(bytesType)
            .setData(ByteString.copyFrom(data))
            .build();

    List<RedactImageRequest.ImageRedactionConfig> imageRedactionConfigs =
        infoTypes
            .stream()
            .map(
                infoType ->
                    RedactImageRequest.ImageRedactionConfig.newBuilder()
                        .setInfoType(infoType)
                        .build())
            .collect(Collectors.toList());

    RedactImageRequest redactImageRequest =
        RedactImageRequest.newBuilder()
            .setParent(ProjectName.of(projectId).toString())
            .addAllImageRedactionConfigs(imageRedactionConfigs)
            .setByteItem(byteContentItem)
            .setInspectConfig(inspectConfig)
            .build();

    RedactImageResponse redactImageResponse = dlpClient.redactImage(redactImageRequest);

    // redacted image data
    ByteString redactedImageData = redactImageResponse.getRedactedImage();
    FileOutputStream outputStream = new FileOutputStream(outputPath);
    outputStream.write(redactedImageData.toByteArray());
    outputStream.close();
  }
}

Node.js

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// Imports the Google Cloud Data Loss Prevention library
const DLP = require('@google-cloud/dlp');

// Imports required Node.js libraries
const mime = require('mime');
const fs = require('fs');

// Instantiates a client
const dlp = new DLP.DlpServiceClient();

// The project ID to run the API call under
// const callingProjectId = process.env.GCLOUD_PROJECT;

// The path to a local file to inspect. Can be a JPG or PNG image file.
// const filepath = 'path/to/image.png';

// The minimum likelihood required before redacting a match
// const minLikelihood = 'LIKELIHOOD_UNSPECIFIED';

// The infoTypes of information to redact
// const infoTypes = [{ name: 'EMAIL_ADDRESS' }, { name: 'PHONE_NUMBER' }];

// The local path to save the resulting image to.
// const outputPath = 'result.png';

const imageRedactionConfigs = infoTypes.map(infoType => {
  return {infoType: infoType};
});

// Load image
const fileTypeConstant =
  ['image/jpeg', 'image/bmp', 'image/png', 'image/svg'].indexOf(
    mime.getType(filepath)
  ) + 1;
const fileBytes = Buffer.from(fs.readFileSync(filepath)).toString('base64');

// Construct image redaction request
const request = {
  parent: dlp.projectPath(callingProjectId),
  byteItem: {
    type: fileTypeConstant,
    data: fileBytes,
  },
  inspectConfig: {
    minLikelihood: minLikelihood,
    infoTypes: infoTypes,
  },
  imageRedactionConfigs: imageRedactionConfigs,
};

// Run image redaction request
try {
  const [response] = await dlp.redactImage(request);
  const image = response.redactedImage;
  fs.writeFileSync(outputPath, image);
  console.log(`Saved image redaction results to path: ${outputPath}`);
} catch (err) {
  console.log(`Error in redactImage: ${err.message || err}`);
}

Python

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def redact_image(project, filename, output_filename,
                 info_types, min_likelihood=None, mime_type=None):
    """Uses the Data Loss Prevention API to redact protected data in an image.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        filename: The path to the file to inspect.
        output_filename: The path to which the redacted image will be written.
        info_types: A list of strings representing info types to look for.
            A full list of info type categories can be fetched from the API.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        mime_type: The MIME type of the file. If not specified, the type is
            inferred via the Python standard library's mimetypes module.
    Returns:
        None; the response from the API is printed to the terminal.
    """
    # Import the client library
    import google.cloud.dlp

    # Instantiate a client.
    dlp = google.cloud.dlp.DlpServiceClient()

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries (protos are also accepted).
    info_types = [{'name': info_type} for info_type in info_types]

    # Prepare image_redaction_configs, a list of dictionaries. Each dictionary
    # contains an info_type and optionally the color used for the replacement.
    # The color is omitted in this sample, so the default (black) will be used.
    image_redaction_configs = []

    if info_types is not None:
        for info_type in info_types:
            image_redaction_configs.append({'info_type': info_type})

    # Construct the configuration dictionary. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        'min_likelihood': min_likelihood,
        'info_types': info_types,
    }

    # If mime_type is not specified, guess it from the filename.
    if mime_type is None:
        mime_guess = mimetypes.MimeTypes().guess_type(filename)
        mime_type = mime_guess[0] or 'application/octet-stream'

    # Select the content type index from the list of supported types.
    supported_content_types = {
        None: 0,  # "Unspecified"
        'image/jpeg': 1,
        'image/bmp': 2,
        'image/png': 3,
        'image/svg': 4,
        'text/plain': 5,
    }
    content_type_index = supported_content_types.get(mime_type, 0)

    # Construct the byte_item, containing the file's byte data.
    with open(filename, mode='rb') as f:
        byte_item = {'type': content_type_index, 'data': f.read()}

    # Convert the project id into a full resource id.
    parent = dlp.project_path(project)

    # Call the API.
    response = dlp.redact_image(
        parent, inspect_config=inspect_config,
        image_redaction_configs=image_redaction_configs,
        byte_item=byte_item)

    # Write out the results.
    with open(output_filename, mode='wb') as f:
        f.write(response.redacted_image)
    print("Wrote {byte_count} to {filename}".format(
        byte_count=len(response.redacted_image), filename=output_filename))

Go

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// redactImage blacks out the identified portions of the input image (with type bytesType)
// and stores the result in outputPath.
func redactImage(w io.Writer, client *dlp.Client, project string, minLikelihood dlppb.Likelihood, infoTypes []string, bytesType dlppb.ByteContentItem_BytesType, inputPath, outputPath string) {
	// Convert the info type strings to a list of InfoTypes.
	var i []*dlppb.InfoType
	for _, it := range infoTypes {
		i = append(i, &dlppb.InfoType{Name: it})
	}

	// Convert the info type strings to a list of types to redact in the image.
	var ir []*dlppb.RedactImageRequest_ImageRedactionConfig
	for _, it := range infoTypes {
		ir = append(ir, &dlppb.RedactImageRequest_ImageRedactionConfig{
			Target: &dlppb.RedactImageRequest_ImageRedactionConfig_InfoType{
				InfoType: &dlppb.InfoType{Name: it},
			},
		})
	}

	// Read the input file.
	b, err := ioutil.ReadFile(inputPath)
	if err != nil {
		log.Fatalf("error reading file: %v", err)
	}

	// Create a configured request.
	req := &dlppb.RedactImageRequest{
		Parent: "projects/" + project,
		InspectConfig: &dlppb.InspectConfig{
			InfoTypes:     i,
			MinLikelihood: minLikelihood,
		},
		// The item to analyze.
		ByteItem: &dlppb.ByteContentItem{
			Type: bytesType,
			Data: b,
		},
		ImageRedactionConfigs: ir,
	}
	// Send the request.
	resp, err := client.RedactImage(context.Background(), req)
	if err != nil {
		log.Fatal(err)
	}
	// Write the output file.
	if err := ioutil.WriteFile(outputPath, resp.GetRedactedImage(), 0644); err != nil {
		log.Fatal(err)
	}
	fmt.Fprintf(w, "Wrote output to %s", outputPath)
}

PHP

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use Google\Cloud\Dlp\V2\DlpServiceClient;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\RedactImageRequest\ImageRedactionConfig;
use Google\Cloud\Dlp\V2\Likelihood;
use Google\Cloud\Dlp\V2\ByteContentItem;

/**
 * Redact sensitive data from an image.
 *
 * @param string $callingProjectId The GCP Project ID to run the API call under
 * @param string $imagePath The local filepath of the image to inspect
 * @param string $outputPath The local filepath to save the resulting image to
 */
function redact_image(
    $callingProjectId,
    $imagePath,
    $outputPath
) {
    // Instantiate a client.
    $dlp = new DlpServiceClient();

    // The infoTypes of information to match
    $phoneNumberInfoType = (new InfoType())
        ->setName('PHONE_NUMBER');
    $infoTypes = [$phoneNumberInfoType];

    // The minimum likelihood required before returning a match
    $minLikelihood = likelihood::LIKELIHOOD_UNSPECIFIED;

    // Whether to include the matching string in the response
    $includeQuote = true;

    // Create the configuration object
    $inspectConfig = (new InspectConfig())
        ->setMinLikelihood($minLikelihood)
        ->setInfoTypes($infoTypes);

    // Read image file into a buffer
    $imageRef = fopen($imagePath, 'rb');
    $imageBytes = fread($imageRef, filesize($imagePath));
    fclose($imageRef);

    // Get the image's content type
    $typeConstant = (int) array_search(
        mime_content_type($imagePath),
        [false, 'image/jpeg', 'image/bmp', 'image/png', 'image/svg']
    );

    // Create the byte-storing object
    $byteContent = (new ByteContentItem())
        ->setType($typeConstant)
        ->setData($imageBytes);

    // Create the image redaction config objects
    $imageRedactionConfigs = [];
    foreach ($infoTypes as $infoType) {
        $config = (new ImageRedactionConfig())
            ->setInfoType($infoType);
        $imageRedactionConfigs[] = $config;
    }

    $parent = $dlp->projectName($callingProjectId);

    // Run request
    $response = $dlp->redactImage($parent, [
        'inspectConfig' => $inspectConfig,
        'byteItem' => $byteContent,
        'imageRedactionConfigs' => $imageRedactionConfigs
    ]);

    // Save result to file
    file_put_contents($outputPath, $response->getRedactedImage());

    // Print completion message
    print('Redacted image saved to ' . $outputPath . PHP_EOL);
}

C#

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        public static object RedactFromImage(string projectId, string originalImagePath, string redactedImagePath)
        {
            var request = new RedactImageRequest
            {
                ParentAsProjectName = new ProjectName(projectId),
                InspectConfig = new InspectConfig
                {
                    MinLikelihood = Likelihood.Likely,
                    Limits = new InspectConfig.Types.FindingLimits() { MaxFindingsPerItem = 5 },
                    IncludeQuote = true,
                    InfoTypes =
                    {
                        new InfoType { Name = "PHONE_NUMBER" },
                        new InfoType { Name = "EMAIL_ADDRESS" }
                    }
                },
                ByteItem = new ByteContentItem
                {
                    Type = ByteContentItem.Types.BytesType.ImagePng,
                    Data = ByteString.FromStream(new FileStream(originalImagePath, FileMode.Open))
                },
            };

            DlpServiceClient client = DlpServiceClient.Create();
            var response = client.RedactImage(request);

            Console.WriteLine($"Extracted text: {response.ExtractedText}");

            // Writes redacted image into file
            response.RedactedImage.WriteTo(new FileStream(redactedImagePath, FileMode.Create, FileAccess.Write));

            return 0;
        }
    }
}