Automated AI Workflow for Fraud and Plagiarism Detection

Automated Fraud and Plagiarism Detection

1. Initial Content Submission

1.1 Content Upload

Authors upload their content through a secure online portal.

1.2 Metadata Input

Authors provide metadata including title, author name, and keywords.

2. Pre-Processing of Content

2.1 Text Normalization

Utilize AI-driven tools like Grammarly to standardize text formatting and correct grammatical errors.

2.2 Language Detection

Employ AI algorithms to detect the language of the submitted content using tools such as Google Cloud Translation API.

3. Plagiarism Detection

3.1 Text Comparison

Implement AI-powered plagiarism detection tools like Turnitin or Copyscape to compare submitted content against a vast database of existing works.

3.2 Similarity Scoring

Generate a similarity score indicating the percentage of content that matches existing sources.

4. Fraud Detection

4.1 Authenticity Verification

Utilize AI tools such as Authorship Attribution algorithms to analyze writing style and assess the likelihood of authorship.

4.2 Anomaly Detection

Implement machine learning models to identify unusual patterns in content submission behavior, flagging submissions that deviate from typical user profiles.

5. Review and Decision Making

5.1 Automated Reporting

Generate comprehensive reports detailing plagiarism and fraud detection results for editorial review.

5.2 Editorial Review

Editors evaluate flagged submissions using AI-assisted tools to make informed decisions regarding publication.

6. Feedback and Improvement

6.1 Author Notification

Communicate the results of the review process to authors, providing feedback on any detected issues.

6.2 Continuous Learning

Utilize feedback to improve AI algorithms and detection accuracy, ensuring the system evolves with changing content trends.

7. Final Publication

7.1 Content Approval

Once cleared of fraud and plagiarism, the content is approved for publication.

7.2 Archiving and Monitoring

Published content is archived, and ongoing monitoring is established to detect any future issues using tools like Plagscan.