How do AI detector tool work?

AI detector tool mainly detect whether text is generated by AI through the following technical principles:
Textual statistical features: Human writing has specific word frequency distributions (such as Zipf's law), sentence length variations, and lexical diversity. AI texts often exhibit a high vocabulary repetition rate (about 1.5 times that of humans), and there are significant differences in the usage patterns of conjunctions.
Semantic coherence features: Human writing often exhibits subtle logical jumps and emotional fluctuations, while AI texts exhibit exceptional smoothness at paragraph transitions. By measuring the "local disturbance sensitivity" of text, it can be found that the probability distribution of AI generated content changes significantly less than that of human text after minor modifications.
Feature of steganography: Large language models leave unique "digital fingerprints" during the generation process. For example, in 2023, a MIT research team discovered that there are specific dimensional neuron activation patterns in the output of GPT-4, which can be identified by a dedicated decoder as "algorithmic watermarks".
The working principle of mainstream AI detector tool includes the following methods:
Deep learning model: By analyzing the deep semantic features of text through deep learning models, it captures the statistical distribution differences between human and AI content. For example, the Mitata AI detector uses a deep learning model to achieve 99% manual text recognition rate and 85% AI content capture rate through 0.65 threshold partitioning.
Adversarial training framework: By training an adversarial network of generators and detectors, the generator learns to create detection blind spots. For example, OpenAI's research shows that a model trained through 2000 rounds of adversarial training can reduce detection accuracy by 42%.
Multi layer cascaded detection architecture: Adopting a multi-layer cascaded detection architecture, it maintains high accuracy for texts containing 5% adversarial noise. For example, some tools maintain a 92% accuracy rate on text containing 5% adversarial noise through a multi-layer cascaded detection architecture.
The application scenarios of AI detector tool include:
Academic paper plagiarism check: detects whether there is plagiarism or AI generated content in academic papers to ensure academic integrity.
Content creation: Help content creators identify and reduce AI traces in text, ensuring the originality of the content.
SEO Content Optimization: Detecting and optimizing SEO content to ensure its naturalness and relevance.
Social media copy detection: Check whether the copy on social media is generated by AI to ensure the authenticity and credibility of the content.