# Plagiarism Detection Engine Overview

## Purpose
The Plagiarism Detection Engine is designed to automatically identify instances of reused content within submissions by comparing them against a extensive database. This module serves as an essential tool for maintaining the integrity and originality of various types of content, such as academic papers, software code, and written works.

## Benefits
- **Efficiency**: Automates the plagiarism checking process, saving significant time compared to manual reviews.
- **Accuracy**: Employs advanced AI techniques to ensure reliable detection across diverse content formats.
- **Scalability**: Capable of handling large volumes of data efficiently, supporting multiple languages and file types (e.g., text, images, PDFs, source code).
- **Integration**: Easily integrates with existing systems or workflows, enhancing overall content management processes.

## Usage Scenarios
### Academic Institutions
- Checking student assignments, theses, and research papers for originality.
- Monitoring integrity in online exams and assessments.

### Content Platforms
- Ensuring originality of user-generated content on forums, blogs, and social media platforms.
- Detecting unauthorized use of copyrighted material.

### Software Development
- Identifying unauthorized reuse of code snippets or libraries in software projects.
- Enforcing coding standards and best practices within teams.

### Online Education
- Preventing academic dishonesty in online courses and degree programs.
- Monitoring discussion forums and collaborative spaces for honest contributions.

### Digital Content Creators
- Protecting original work from unauthorized duplication across various media platforms.

This module offers a robust solution for developers seeking to integrate plagiarism detection into their applications, ensuring content authenticity and integrity with ease. Its scalability and adaptability make it suitable for a wide range of scenarios, enhancing the trustworthiness of digital content.

Text Comparison

The module uses advanced algorithms to compare text submissions against a large corpus of reference materials. It supports multiple file formats, including .docx, PDF, and plain text.

Corpus Management

Manages a centralized repository of documents for comparison. Supports dynamic updates and custom filtering based on document type, source, or language.

Machine Learning Integration

Employs AI models to analyze and detect patterns in text, improving accuracy over time with continuous learning from new data inputs.

Performance and Scalability

Optimized for high-speed processing of large datasets, ensuring quick results even when analyzing massive volumes of content.

Language Support

Detects plagiarism across multiple languages, including English, Spanish, French, German, and more, using language-specific models.

Customization Options

Allows developers to configure parameters such as similarity thresholds, exclusion rules, and custom dictionaries for tailored detection needs.

Reporting API

Provides a programmatic interface to retrieve detailed reports of detected similarities, with options to format results in JSON or XML.

Version Control Integration

Interfaces with version control systems (e.g., Git) to track changes in submissions over time, aiding in detecting incremental plagiarism.

Technical Documentation for Plagiarism Detection Engine

Module Overview

The Plagiarism Detection Engine is designed to identify content reuse across submissions by comparing them against a large corpus. It leverages machine learning techniques for accurate detection.

1. FastAPI Endpoint (Backend)

This endpoint accepts file uploads and returns plagiarism detection results.

from fastapi import APIRouter, UploadFile, File
import os

router = APIRouter()

class FileUpload(BaseModel):
    file: bytes = File(...)
    filename: str = File(...)

@router.post("/detect-plagiarism", response_model=PlagiarismResult)
async def upload_file(
    file: UploadFile,
    current_user: User = Depends(oauth2_passwordBearer())
):
    """Endpoint for uploading files and detecting plagiarism."""
    
    # Save uploaded file temporarily
    temp_dir = "temp_files"
    os.makedirs(temp_dir, exist_ok=True)
    file_path = os.path.join(temp_dir, file.filename)
    
    with open(file_path, "wb") as f:
        content = await file.read()
        f.write(content)
    
    # Perform plagiarism check
    result = await plagiarism_check(file_path)
    
    return PlagiarismResult(
        submission_id=result["submission_id"],
        similarity_percent=result["similarity_percent"],
        matched_content=result["matched_content"]
    )

# Example usage:
"""
curl -X POST "http://localhost:8000/detect-plagiarism" \
     -H "Content-Type: multipart/form-data" \
     --form "file=@/path/to/your/file.txt"
"""

2. React UI Component (Frontend)

A simple file upload component for submitting content to the API.

import React, { useState } from 'react';
import axios from 'axios';
import { useDropzone } from 'react-dropzone';

const PlagiarismChecker = () => {
    const [files, setFiles] = useState([]);
    const [results, setResults] = useState([]);

    const onDrop = useCallback((acceptedFiles) => {
        const formData = new FormData();
        acceptedFiles.forEach(file => {
            formData.append('file', file);
        });

        axios.post('/api/detect-plagiarism', formData, {
            headers: {
                'Content-Type': 'multipart/form-data'
            }
        })
        .then(response => {
            setResults(response.data);
        });
    }, []);

    const { getRootProps, getInputProps } = useDropzone({
        onDrop,
        accept: {
            'text/plain': ['.txt'],
            'application/pdf': ['.pdf']
        }
    });

    return (
        <div className="plagiarism-checker">
            <input {...getInputProps()} />
            <div {...getRootProps()}>
                {files.length === 0 ? (
                    <p>Drag & drop files here, or click to select files</p>
                ) : (
                    <p>{files.length} files selected</p>
                )}
            </div>
            
            {results.map((result) => (
                <div key={result.submission_id}>
                    <h3>Submission ID: {result.submission_id}</h3>
                    <p>Similarity: {result.similarity_percent}%</p>
                    <pre>{JSON.stringify(result.matched_content, null, 2)}</pre>
                </div>
            ))}
        </div>
    );
};

export default PlagiarismChecker;

3. Data Schema (Pydantic)

Define the data models for input and output.

from pydantic import BaseModel

class FileUpload(BaseModel):
    file: bytes
    filename: str

class PlagiarismResult(BaseModel):
    submission_id: str
    similarity_percent: float
    matched_content: dict

# Example request body:
"""
{
    "file": "base64_encoded_file_content",
    "filename": "submission.txt"
}
"""

# Example response body:
"""
{
    "submission_id": "12345",
    "similarity_percent": 89.5,
    "matched_content": {
        "source_id": "abc123",
        "content片段": "This is the matched content..."
    }
}
"""

Usage Example

API Call:

curl -X POST http://localhost:8000/detect-plagiarism \
     -H 'Content-Type: multipart/form-data' \
     --form file=@/path/to/file.txt

React Component Output:

The React component will display the submission ID, similarity percentage, and matched content in a user-friendly format.

Notes

• The FastAPI endpoint supports both text files (.txt) and PDFs ( .pdf`).
• The React component uses react-dropzone for drag-and-drop functionality.
• The Pydantic models ensure proper validation of input and output data.

Plagiarism Detection Engine Module Documentation

Overview

The Plagiarism Detection Engine is an AI-powered module designed to identify reused content within submissions by comparing them against a large corpus of known documents. This module is particularly useful for detecting academic plagiarism, software code duplication, and other forms of content reuse.

Related Modules

• Text Comparison Module: For advanced text similarity calculations.
• File Processing Module: Handles various file formats (PDFs, Word docs, etc.) for input processing.
• Corpus Management Module: Manages and updates the corpus of reference documents.
• Alerting Module: Triggers notifications based on detected plagiarism levels.

Use Cases

1. Academic Plagiarism Detection: Detects copied content in student submissions or research papers.
2. Source Code Duplication: Identifies reused code segments in software development projects.
3. Document Comparison: Compares large documents (e.g., books, reports) for content reuse.

Integration Tips

1. Preprocessing:
   • Ensure all input files are converted to a common format (e.g., text or HTML).
2. Corpus Setup:
   • Regularly update the corpus with new reference documents.
3. Performance Optimization:
   • Use asynchronous processing for large-scale submissions.
4. Avoid False Positives:
   • Implement context-aware comparison to reduce false alarms.
5. Error Handling:
   • Log and handle errors gracefully (e.g., corrupted files or timeouts).

Configuration Options

Example Configuration

{
  "enabled": true,
  "similarity_threshold": 0.85,
  "token_size": "word",
  "exclusion_patterns": ["student_name", "institution_name"],
  "api_key": "your_api_key_here",
  "cache_enabled": true,
  "cache_expiry": 48,
  "verbose_logging": false
}

Conclusion

The Plagiarism Detection Engine is a powerful tool for identifying content reuse. By leveraging AI and integration with related modules, it provides developers with a robust solution for detecting plagiarism in various contexts.