Detection Methodology
Last updated: March 2026 | Technical documentation of EyeSift's AI detection methods
Methodology Overview
This document provides a comprehensive technical explanation of the methods, algorithms, and analytical frameworks EyeSift uses to detect AI-generated content across text, images, audio, and video. Our approach combines statistical pattern analysis with machine learning techniques, drawing on established research from leading academic institutions.
We publish this methodology because we believe transparency about how AI detection works, including its strengths and weaknesses, builds the trust that this technology requires to be used responsibly.
1. AI Detection Technology Explained
AI detection fundamentally involves distinguishing between content produced by humans and content produced by generative AI models. This is a classification problem where the detector must identify subtle statistical, structural, and semantic differences between human and machine output.
Perplexity Analysis
Perplexity measures how "surprised" a language model is by a given text. Human-written text tends to exhibit higher and more variable perplexity because people make creative, idiosyncratic word choices. AI-generated text gravitates toward low-perplexity outputs because language models select high-probability tokens.
EyeSift calculates perplexity scores at multiple granularities: per-token, per-sentence, and per-paragraph. Research from the University of Maryland demonstrated that perplexity-based detection alone can achieve approximately 70-80% accuracy on GPT-3.5 and GPT-4 output.
Burstiness Analysis
Burstiness quantifies the variation in sentence-level complexity within a document. Human writers naturally alternate between short, direct sentences and longer, more intricate constructions. AI-generated text tends to maintain a more uniform level of complexity.
We measure burstiness using the coefficient of variation of sentence-level features including sentence length, syntactic depth, vocabulary sophistication, and information density. Research from Stanford NLP Group has shown that burstiness is particularly effective at detecting text from instruction-tuned models.
Neural Classification
EyeSift employs transformer-based neural classifiers fine-tuned on large datasets of verified human-written and AI-generated text. These capture distributional patterns difficult to express as explicit rules.
Ensemble Approach
EyeSift combines all three approaches in an ensemble architecture. The ensemble's meta-classifier weights these signals based on input characteristics to produce a final confidence score more robust than any individual method.
2. Multi-Modal Detection
EyeSift analyzes text, images, audio, and video. Each modality requires distinct detection approaches:
Text Detection
Combination of perplexity analysis, burstiness scoring, and neural classification. Produces both document-level confidence scores and per-section highlights.
Image Detection
AI-generated images leave statistical fingerprints differing from photographs:
- GAN fingerprints: Spectral analysis reveals periodic artifacts from upsampling layers
- EXIF metadata: Genuine photographs contain camera-specific metadata; AI images lack this
- Semantic consistency: Lighting, shadows, reflections, anatomical proportions
- Noise patterns: Camera sensors produce characteristic noise; AI images differ
Video Detection
Deepfake detection extends image analysis with temporal analysis:
- Temporal consistency: Frame-to-frame facial landmark analysis
- Audio-visual sync: Lip movement timing relative to audio track
- Biological signals: Micro-expressions, blink patterns, physiological movements
Audio Detection
Voice cloning and TTS systems produce detectable artifacts:
- Spectral analysis: Frequency spectrum differences between natural and synthesized audio
- Prosodic patterns: Pitch, timing, emphasis, rhythm variations
- Breathing patterns: Natural speech includes breathing sounds at predictable intervals
3. Accuracy Benchmarks
EyeSift's stated accuracy range of 75-85% is based on systematic testing. Accuracy varies by content type, AI model, and text characteristics.
Standard AI Text
Unmodified GPT-4/Claude output: ~82-88% detection rate
Paraphrased AI Text
AI text through paraphrasing tools: ~60-70% detection rate
Human Text (True Negatives)
Human text correctly identified: ~88-94% specificity
Non-Native English
False positive rate on non-native writing: ~8-15%
These figures represent measured performance as of March 2026, re-evaluated monthly.
4. Limitations and Known Weaknesses
We believe transparency about limitations is more valuable than projecting false confidence.
Fundamental Limitations
- Theoretical ceiling: As language models improve, detection becomes mathematically harder
- Short text degradation: Accuracy decreases significantly for texts shorter than ~150 words
- Domain sensitivity: Performs best on general-purpose text, less on specialized content
- Language coverage: Optimized for English; other languages have lower accuracy
Known False Positive Triggers
- Non-native English writing with simple vocabulary
- Heavily edited or professionally copyedited text
- Formulaic writing: press releases, product descriptions, legal boilerplate
- Technical writing with specialized terminology
5. Academic References
- Mitchell, E., et al. "DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature." ICML 2023
- Sadasivan, V.S., et al. "Can AI-Generated Text be Reliably Detected?" arXiv:2303.11156, 2023
- Kirchenbauer, J., et al. "A Watermark for Large Language Models." ICML 2023
- Wang, S.Y., et al. "CNN-generated images are surprisingly easy to spot...for now." CVPR 2020
- Rossler, A., et al. "FaceForensics++: Learning to Detect Manipulated Facial Images." ICCV 2019
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