The Copyleaks Cross-Language Detection is a powerful feature that enables you to identify plagiarism across different languages. It can detect content that has been translated from one language to another, helping catch sophisticated plagiarism attempts where text is copied, translated, and presented as original work.

This guide will walk you through the process of using Cross-Language Detection and understanding its capabilities.

Overview

Cross-Language Detection identifies content that has been translated from one language to another. For example, if someone takes content written in English, translates it to Spanish, and presents it as original work, Cross-Language Detection can identify this plagiarism attempt.

Supported Languages View the complete list of supported source and result languages for cross-language detection.

Submit for Analysis Learn how to submit content for plagiarism detection including cross-language detection.

How It Works

Cross-Language Detection uses advanced translation and semantic matching technology to:

1. Analyze Source Document: The system processes your submitted document in its original language.

2. Translation Analysis: The content is analyzed across different language databases.

3. Semantic Matching: Beyond direct translation, the system looks for semantic similarities that might indicate translated plagiarism.

4. Results Compilation: Findings are compiled into a comprehensive report that identifies potential matches across languages.

Key Benefits

• Catch Sophisticated Plagiarism: Identify plagiarism attempts that involve translation, which traditional plagiarism checkers would miss.
• Multi-Language Support: Support for multiple source languages and an even wider range of result languages.
• Seamless Integration: Use the same API workflow as regular plagiarism checks with additional parameters.
• Detailed Reporting: Get precise information about cross-language matches with the same detailed reporting as standard plagiarism detection.

🚀 Get Started

1. Before you begin

   Before you start, ensure you have the following:

   • An active Copyleaks account. If you don’t have one, sign up for free.
   • You can find your API key on the API Dashboard.

2. Installation

   Choose your preferred method for making API calls.

   HTTP

   You can interact with the API using any standard HTTP client.

   For a quicker setup, we provide a Postman collection. See our Postman guide for instructions.

   cURL
   Ubuntu/Debian

   sudo apt-get install curl

   Windows

   Download it from curl.se

   macOS

   brew install curl

   Python

   pip install copyleaks

   JavaScript

   npm install plagiarism-checker

   Java

   Download from Maven

3. Login

   To perform a scan, we first need to generate an access token. For that, we will use the login endpoint. The API key can be found on the Copyleaks API Dashboard.

   Upon successful authentication, you will receive a token that must be attached to subsequent API calls via the Authorization: Bearer <TOKEN> header. This token remains valid for 48 hours.

   HTTP

   POST https://id.copyleaks.com/v3/account/login/api
   
   Headers
   Content-Type: application/json
   
   Body
   {
       "email": "[email protected]",
       "key": "00000000-0000-0000-0000-000000000000"
   }

   cURL

   export COPYLEAKS_EMAIL="[email protected]"
   export COPYLEAKS_API_KEY="your-api-key-here"
   
   curl --request POST \
     --url https://id.copyleaks.com/v3/account/login/api \
     --header 'Accept: application/json' \
     --header 'Content-Type: application/json' \
     --data "{
       \"email\": \"${COPYLEAKS_EMAIL}\",
       \"key\": \"${COPYLEAKS_API_KEY}\"
     }"

   Python

   from copyleaks.copyleaks import Copyleaks
   
   EMAIL_ADDRESS = "[email protected]"
   API_KEY = "your-api-key-here"
   
   # Login to Copyleaks
   auth_token = Copyleaks.login(EMAIL_ADDRESS, API_KEY)
   print("Logged successfully!\nToken:", auth_token)

   JavaScript

   const { Copyleaks } = require("plagiarism-checker");
   
   const EMAIL_ADDRESS = "[email protected]";
   const API_KEY = "your-api-key-here";
   const copyleaks = new Copyleaks();
   
   // Login function
   function loginToCopyleaks() {
     return copyleaks.loginAsync(EMAIL_ADDRESS, API_KEY).then(
       (loginResult) => {
         console.log("Login successful!");
         console.log("Access Token:", loginResult.access_token);
         return loginResult;
       },
       (err) => {
         console.error('Login failed:', err);
         throw err;
       }
     );
   }
   
   loginToCopyleaks();

   Java

   import com.copyleaks.sdk.api.Copyleaks;
   
   String EMAIL_ADDRESS = "[email protected]";
   String API_KEY = "00000000-0000-0000-0000-000000000000";
   
   // Login to Copyleaks
   try {
       String authToken = Copyleaks.login(EMAIL_ADDRESS, API_KEY);
       System.out.println("Logged successfully!\nToken: " + authToken);
   } catch (CommandException e) {
       System.out.println("Failed to login: " + e.getMessage());
       System.exit(1);
   }

   Response

   {
       "access_token": "<ACCESS_TOKEN>",
       ".issued": "2025-07-31T10:19:40.0690015Z",
       ".expires": "2025-08-02T10:19:40.0690016Z"
   }

   Note

   Save this token! It’s valid for 48 hours and can be reused for subsequent API calls.

4. Submit for Cross-Language Analysis

   Submission Methods

   You can submit content for analysis using multiple methods:

   File Upload

   Submit documents including PDF, DOCX, TXT and other formats for scanning

   URL Scanning

   Submit webpages and online documents directly by providing their URL

   Text Images

   Extract and scan text from images including screenshots and photos

   For this guide, we’ll demonstrate document submission for writing assessment. Each submission requires a unique scanId for proper tracking and identification.

   Tip

   For testing, set "sandbox": true. Sandbox mode is free and returns mock results.

   HTTP

   POST https://api.copyleaks.com/v3/scans/submit/file/{scanId}
   Content-type: multipart/form-data
   Authorization: Bearer YOUR_LOGIN_TOKEN
   
   Request Body:
   {
     "base64": "<BASE64_ENCODED_FILE>",
     "filename": "my-document.pdf",
     "properties": {
       "sandbox": true,
       "scanning": {
         "crossLanguages": {
           "languages": [
             { code: "es" },
             { code: "fr" }
           ]
         }
       }
     }
   }

   cURL

   curl -X POST "https://api.copyleaks.com/v3/scans/submit/file/my-scan-123" \
        -H "Authorization: Bearer <YOUR_AUTH_TOKEN>" \
        -H "Content-Type: multipart/form-data" \
        -F "[email protected]" \
        -F 'properties={
            "sandbox": true,
            "scanning": {
              "crossLanguages": {
                "languages": [
                   { code: "es" },
                   { code: "fr" }
                ]
              }
            }
          }'

   Python

   from copyleaks.copyleaks import Copyleaks
   from copyleaks.models.submit.document import FileDocument
   from copyleaks.models.submit.properties.scan_properties import ScanProperties
   
   scan_id = "my-scan-123"
   file_path = "my-document.pdf"
   
   # Create document to scan
   file_submission = FileDocument(file_path)
   file_submission.set_sandbox(True)
   
   # Configure cross-language detection
   properties = ScanProperties()
   properties.set_scanning_cross_languages(["es", "fr"])  # Spanish and French
   file_submission.set_properties(properties)
   
   # Submit for scanning
   response = Copyleaks.submit_file(auth_token, scan_id, file_submission)
   print(response)

   JavaScript

   const { Copyleaks, CopyleaksFileSubmissionModel } = require('plagiarism-checker');
   
   async function submitWithCrossLanguage() {
       try {
           // Initialize Copyleaks
           const copyleaks = new Copyleaks();
   
           // Login to get the authentication token
           const loginResult = await copyleaks.loginAsync('[email protected]', 'YOUR_API_KEY');
   
           const scanId = `cross-lang-scan-${Date.now()}`;
   
           // Create a file submission model
           const fileToSubmit = './my-document.pdf';
           const submission = new CopyleaksFileSubmissionModel(fileToSubmit);
           submission.sandbox = true;
   
           // Set cross language properties
           submission.properties = {
               scanning: {
                   crossLanguages: {
                       languages: [
                           { code: "es" },
                           { code: "fr" }
                       ]
                   }
               }
           };
   
           // Submit the file for scanning
           const response = await copyleaks.submitFileAsync(loginResult, scanId, submission);
           console.log("Submission successful:", response);
   
       } catch (error) {
           console.error("An error occurred:", error);
       }
   }
   
   submitWithCrossLanguage();

Supported Languages

Cross-Language Detection supports a wide range of languages:

• Source Languages: The document you upload can be in one of the supported source languages (Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish).

• Result Languages: Copyleaks can detect plagiarism in over 30 target languages, including Albanian, Bulgarian, Chinese, Czech, German, Greek, Hindi, Japanese, Korean, and many more.

Tip

The list of supported languages is continually expanding. For the most up-to-date list, use the Supported Cross-Languages API.

Pricing

Cross-Language Detection uses additional credits based on the following model:

1. Base Scan: The base scan in the document’s original language counts as normal (1 credit per 250 words).

2. Additional Languages: Each additional language selected for cross-language detection will incur the same credit cost as the base scan.

For example, if your document is 1,000 words (4 credits) and you select two additional languages for cross-language detection (Spanish and French), the total cost would be:

• Base scan: 4 credits
• Spanish: 4 credits
• French: 4 credits
• Total: 12 credits

Note

For precise credit calculation, use the Price Check Before Scan feature to get an exact quote before proceeding with the scan.

Use Cases

Cross-Language Detection is particularly valuable in several scenarios:

• Academic Institutions: Universities with international student bodies can detect plagiarism regardless of the original content’s language.

• Global Publishing: Publishers that operate in multiple regions can ensure content originality across language barriers.

• Research Verification: Researchers can verify the originality of work when citing sources from different languages.

• Content Licensing: Media companies can protect their intellectual property from unauthorized translations.

Best Practices

To maximize the effectiveness of Cross-Language Detection:

1. Select Relevant Languages: Choose only the languages that are relevant to your use case to optimize credit usage.

2. Use with Regular Plagiarism Detection: Cross-Language Detection works best as a complement to standard plagiarism detection.

3. Review Results Carefully: Because translation can alter sentence structure and word choice, review cross-language matches with special attention to semantic similarity rather than exact matches.