The technical principle of check AI

The technical principles of check AI mainly include the following aspects:
Data preprocessing and feature extraction: The raw content needs to be transformed into machine understandable numerical representations. For example, text generates semantic features through word segmentation and word vector models (such as BERT); Using Convolutional Neural Networks (CNN) to extract visual features such as color and texture from images; Audio features are generated through spectrogram analysis or speech to text processing.
Deep learning models: Based on supervised learning (such as classifiers) or unsupervised learning (such as anomaly detection), the model can identify specific types of violating content. Pre trained large models such as Transformer and CLIP perform well in multilingual and multimodal scenarios due to their powerful generalization ability.  
Multimodal fusion: Modern content often combines text, images, and audio, and AI integrates multidimensional features through attention mechanisms. For example, detecting illegal content in short videos requires analyzing the visuals, subtitles, and background music simultaneously.
Real time processing and dynamic updates: Streaming data processing frameworks (such as Apache Kafka) and incremental learning techniques ensure that the system can respond in real-time and quickly adapt to new violation patterns, such as evading detection of malicious content through spelling variants.
Decision making and post-processing: After the model outputs the risk probability, it combines business rules to make the final judgment. For example, Turnitin's AI detection function is based on the OpenAI GPT recognition model, which analyzes the "likelihood of generation" (Perplexity+Burstiness) of text to determine whether it is machine written text.
Multi dimensional feature analysis: The core technology of CNKI AIGC detection system lies in multi-dimensional feature analysis. The system not only analyzes the surface features of text such as word frequency and sentence length, but also captures the deep features of AI writing, including abnormal logical coherence and strong citation format regularity.