Using generative adversarial network to improve the accuracy of detecting AI-generated tweets
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel AI-driven method for detecting AI-generated text, achieving 99.60% accuracy by using generative models and ensemble learning for precise text attribute characterization.
Area Of Science
- Computer Science
- Artificial Intelligence
- Natural Language Processing
Background
- The proliferation of AI-generated text necessitates robust detection methods.
- Existing machine learning techniques require enhancement for accurate AI text identification.
Purpose Of The Study
- To develop a novel approach for improving the accuracy of AI-generated text detection.
- To leverage generative AI capabilities and ensemble learning for precise text attribute characterization.
Main Methods
- Text preprocessing including noise removal, tokenization, stop-word removal, and normalization.
- Feature engineering representing text as matrices capturing word correlations and weights.
- Generative Adversarial Network (GAN)-based feature extraction.
- Weighted Random Forest (RF) model for final text classification.
Main Results
- The proposed methodology achieved an average accuracy of 99.60% in distinguishing human-written from AI-generated text.
- Demonstrated a 1.5% improvement in accuracy compared to existing methods.
- Successfully characterized attributes specific to AI-generated content.
Conclusions
- The novel approach effectively enhances AI-generated text detection accuracy.
- Combining generative AI with ensemble methods offers a powerful solution for content authenticity.
- The method provides a significant advancement in identifying AI-created content.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
