AIGC Detection Records: How to Scientifically Identify and Respond to AI-Generated Content

In today’s era of explosive digital content growth, AI-generated content (AIGC) has penetrated various fields including academia, media, and commerce. How to effectively detect AIGC and ensure content authenticity has become an urgent problem for academic circles and content platforms. This article will deeply explore the core technologies, application scenarios, and practical tools of AIGC detection, helping users establish systematic detection processes.

1. AIGC Detection Technology Principles and Classification

1.1 Text Feature-Based Detection Methods

AI-generated text often has specific statistical characteristics, such as low vocabulary diversity and overly regular sentence structures. Detection tools analyze the following dimensions for judgment:

• Perplexity: Measures the unpredictability of text, with AI-generated content typically showing abnormally low perplexity
• Burstiness: Human writing naturally alternates between long and short sentences, while AI text often maintains uniform sentence length
• Semantic Coherence: Deep analysis of logical connections between paragraphs to identify possible contextual breaks in AI content

1.2 Multimodal Content Detection Technology

For non-text AIGC such as images and videos, mainstream detection methods include:

• Metadata Analysis: Examines digital fingerprints such as EXIF information and generation history of files
• Frequency Domain Feature Detection: Identifies frequency domain anomalies specific to AI images through Fourier transform
• Biological Feature Verification: Analyzes physiological signals such as micro-expressions and pupil changes of characters in videos

2. Typical Application Scenarios and Response Strategies

2.1 Academic Paper Detection

When educational institutions use professional tools, they can enhance AIGC identification through the following methods:

• Set mixed detection modes, simultaneously conducting traditional plagiarism checks and AI content analysis
• Focus on literature review sections, where AI-generated reviews often show conceptual stacking without deep connections
• Verify the authenticity of references, as some AI tools fabricate citation sources

2.2 New Media Content Review

Content platforms can adopt hierarchical detection mechanisms:

• Primary filtering: Real-time detection based on API to handle massive UGC content
• Deep analysis: Manual review of suspected content, combined with creator historical behavior assessment
• Dynamic learning: Establish continuously updated detection model libraries to respond to new generation tools

3. quillbot’s AIGC Detection Solution

quillbot’s newly launched AI detection module includes three core functions:

3.1 Multidimensional Detection Report

The system-generated detection report not only labels suspected AI-generated paragraphs but also provides:

• Content originality score (0-100 point system)
• Writing style consistency analysis
• Similarity comparison with public AI training data

3.2 Dynamic Threshold Adjustment

According to different disciplinary characteristics, users can:

• Customize detection sensitivity (three levels: lenient/standard/strict)
• Exclude sections with concentrated professional terminology (such as methodology parts)
• Set whitelists to protect reasonably quoted content

3.3 Modification Suggestion System

When AI-generated content is detected, the tool intelligently provides:

• Sentence restructuring solutions (changing expression methods while maintaining original meaning)
• Academic expression optimization suggestions
• Recommendations for relevant literature to assist in enhancing originality

4. Management and Application of Detection Records

Establishing systematic AIGC detection records helps long-term content quality management:

4.1 Standardized Record Format

Complete detection records should include:

• Detection timestamp and tool version
• Original text hash value (ensuring content has not been tampered with)
• Detailed detection parameter configuration
• Result confidence indicators

4.2 Institutional Application Cases

A certain university graduate school achieved through the quillbot system:

• Full-process AI content monitoring of degree theses
• Establishment of differentiated detection standards by discipline
• Generation of annual academic integrity reports to guide teaching reform

4.3 Usage Suggestions for Individual Researchers

Scholars can adopt the following practices in daily scientific research:

• Regularly self-check written content (recommended monthly)
• Save detection records at key nodes as supporting materials
• Require contributors to provide original detection reports when participating in academic reviews