Detection Methodology

Last updated: March 2026 | Technical documentation of EyeSift's AI detection methods

AI Detection Technology

EyeSift combines statistical pattern analysis with machine learning techniques to detect AI-generated content. Our approach uses three complementary methods: perplexity analysis, burstiness scoring, and neural classification, combined in an ensemble architecture.

Perplexity Analysis

Perplexity measures how "surprised" a language model is by a given text. Human-written text exhibits higher and more variable perplexity due to creative and unexpected word choices. AI-generated text gravitates toward low-perplexity outputs. EyeSift calculates perplexity at per-token, per-sentence, and per-paragraph granularities.

Burstiness Analysis

Burstiness quantifies variation in sentence-level complexity. Human writers alternate between short and long sentences with varying complexity. AI-generated text maintains more uniform complexity. We measure burstiness using coefficient of variation of sentence length, syntactic depth, vocabulary sophistication, and information density.

Neural Classification

Transformer-based neural classifiers fine-tuned on large datasets of verified human and AI text capture distributional patterns invisible to statistical methods. Based on architectures similar to RoBERTa and DeBERTa.

Multi-Modal Detection

EyeSift detects AI content across text, images (GAN fingerprints, EXIF analysis), video (temporal consistency, deepfake artifacts), and audio (spectral analysis, prosodic patterns). Each modality uses specialized detection techniques.

Accuracy Benchmarks

Our 75-85% accuracy range is based on systematic testing. Standard AI text: ~82-88% detection. Paraphrased AI text: ~60-70%. Human text specificity: ~88-94%. Benchmarks use k-fold cross-validation with stratified sampling.

Limitations

We acknowledge known weaknesses: detection degrades for short texts (<150 words), paraphrased content, adversarial prompts, and non-English text. False positives can occur with non-native English writing and formulaic text genres.

Academic References

Our methodology draws on research from Mitchell et al. (DetectGPT, ICML 2023), Sadasivan et al. (theoretical limits, 2023), Kirchenbauer et al. (watermarking, ICML 2023), Wang et al. (CNN fingerprints, CVPR 2020), and other peer-reviewed work from Stanford, MIT, and Princeton.