Human–AI Content Source Detection Using Dual Classification Models
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Solution Overview
Problem
Distinguishing between human-generated and AI-generated digital content, particularly in the context of software code, has become challenging due to the capabilities of Generative AI engines like ChatGPT, leading to issues with plagiarism and copyright infringement.
Innovation Solution
A system and method utilizing supervised learning techniques to train human and computer classification models on known digital content samples, allowing for the analysis of new content to predict the source of creation, with a focus on differentiating between human and GenAI contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Generative AI engines are used to produce digital content, then productivity and data generation speed are improved, but the ability to distinguish human-generated from AI-generated content deteriorates
Solution Approach 1:
The system performs preliminary classification by training separate human and computer classification models on digital content samples before actual analysis. These pre-trained models enable rapid differentiation of content sources without compromising accuracy, resolving the contradiction between fast AI content generation and accurate source identification.
Solution Approach 2:
The classification system is segmented into multiple specialized models: a human classification model trained on human-generated content and a computer classification model trained on AI-generated content. This segmentation allows each model to specialize in detecting specific patterns, improving overall differentiation accuracy while maintaining efficient processing speed.
2Measurement precision
If multiple classification models are trained on digital content samples, then measurement precision for source identification is improved, but device complexity increases
Solution Approach 1:
The classification models are designed to be universal and adaptable to different types of digital content (text, code, images, audio). The same human and computer classification models can analyze various content formats by training on diverse samples, reducing the need for separate specialized models for each content type and thereby managing system complexity.
Solution Approach 2:
The system uses copying of training samples and model architectures to create multiple instances of classification models. Instead of designing entirely new models for each scenario, the system replicates and adapts proven model structures, simplifying the development and deployment process while maintaining high identification accuracy.
3Reliability
If digital content is analyzed with multiple classification models, then reliability of credit assignment is improved, but analysis time and computational resources increase
Solution Approach 1:
The system applies partial analysis by focusing on the most discriminative features and portions of digital content that best indicate human or AI generation. Rather than analyzing every aspect of the content equally, the models concentrate on key patterns, reducing computational overhead and analysis time while maintaining reliable credit assignment.
Solution Approach 2:
The classification models incorporate feedback mechanisms where analysis results from one model inform the processing in another. The human and computer classification models work iteratively, with feedback loops that allow the system to converge on accurate credit assignment more efficiently, reducing overall analysis time while improving reliability.
Data Source
AI summary
Systems and methods are provided for predicting whether digital content is generated by a human or by a machine. In one implementation, a method includes a step of receiving digital content to be tested. The method further includes a step of analyzing the digital content with respect to both a human classification model associated with a specific individual and a computer classification model associated with a specific Generative Artificial Intelligence (GenAI) engine. In addition, based on results of analyzing the digital content, the method includes a step of predicting whether credit for creating the digital content is to be assigned to the specific individual or the GenAI engine.