Scan text, images, and code for statistical watermarks embedded by Claude, GPT, Gemini, and other AI models. Get instant confidence scores and detailed technical analysis.
Our engine performs 47 distinct statistical tests on your content, analyzing token distributions, n-gram frequencies, entropy patterns, and perplexity scores across multiple model architectures. Each test is weighted based on the target model's known watermarking strategy to maximize detection accuracy while minimizing false positives.
For text, the honest picture is this: statistical token watermarks (like the one Claude added in 2026) are embedded in word choice and can only be verified with the private key held by the model's maker. Public tools cannot reliably attribute or read them. What we can do is find hidden characters, strip metadata, and — if you choose — rewrite text to reduce watermark signal.
Every scan produces a multi-dimensional confidence score. Rather than a single percentage, you receive breakdowns across detection certainty, model attribution confidence, watermark density, and estimated removal difficulty. Scores above 85% indicate strong watermark presence, while scores below 30% suggest genuine human authorship with high reliability.
AI watermarks represent one of the most sophisticated approaches to content provenance and authenticity verification in the generative AI era. Unlike traditional digital watermarks that embed visible or semi-visible marks in images, AI text watermarks operate at the statistical level, modifying the probability distributions used during text generation to create detectable patterns.
Anthropic's Claude uses a watermarking approach that adjusts token sampling probabilities in a way that is imperceptible to human readers but statistically measurable by detection algorithms. The key insight is that natural human text exhibits specific distributional properties that differ from watermarked AI text in ways that become apparent over sufficient sample sizes, typically 200 words or more.
Our detector analyzes these distributional differences across multiple dimensions simultaneously, combining traditional statistical hypothesis testing with machine learning classifiers trained on millions of labeled examples. The result is a detection system that maintains high accuracy even on short texts, mixed human-AI content, and adversarially modified samples.
For teams managing content at scale, watermark detection is becoming an essential component of editorial workflows, compliance pipelines, and quality assurance processes. Whether you need to verify the provenance of submitted articles, ensure AI-disclosure compliance, or audit your own AI-assisted content pipelines, our detector provides the precision and reliability required for production use.