AI Watermark Detector – Hidden Character & Fingerprint Scanner

Detect invisible Unicode characters, AI watermarks, and hidden fingerprints embedded in text. This free online tool uncovers zero-width spaces, steganographic encodings, Unicode tag characters, and typographic patterns used by ChatGPT, Claude, Gemini, GitHub Copilot, and other AI systems for tracking, attribution, and identification.

Key Features

Comprehensive Detection

Detects 90+ invisible and non-printing Unicode characters, including zero-width spaces, joiners, directional overrides, Hangul fillers, and Unicode tag characters (U+E0000–U+E007F)
Binary watermark detection to identify steganographic data encoded via invisible characters
Typographic fingerprint analysis covering smart quotes, em/en dashes, and Unicode ellipsis

AI Model Identification

The tool estimates the most likely AI model behind the text by correlating multiple signals:

ChatGPT / GPT-4: Zero-width watermarks, em-dash usage, smart quotes, and phrases like “delve into”
Claude: Cleaner output, en-dash preference, conversational phrasing
Gemini: Unicode tag characters and highly structured formatting
GitHub Copilot: Code-heavy content combined with GPT-style linguistic patterns

Model detection is probabilistic and becomes more accurate as multiple indicators align.

Visual Content Map

An interactive content map highlights exactly where hidden characters occur:

Red markers for invisible or dangerous characters
Yellow markers for typographic fingerprint characters
Blue markers for special or non-standard whitespace

Each marker reveals the Unicode code point and character type.

One-Click Cleaning

Generate watermark-free text instantly:

Removes invisible and non-printing characters
Preserves word spacing and paragraph structure
Optional typography normalization (smart quotes → straight quotes, em/en dashes → hyphens, ellipsis → three dots)
Download the cleaned output as a text file

File Upload Support

Clipboard operations often strip invisible Unicode characters. Uploading a file preserves every byte, ensuring accurate detection.

What Are AI Watermarks?

AI watermarks are hidden markers embedded in text generated by artificial intelligence systems. These invisible fingerprints are used for:

Content attribution and provenance tracking
Monitoring distribution and reuse
Copyright and ownership verification
Regulatory and compliance requirements

Most watermarking systems rely on invisible Unicode characters such as zero-width spaces and joiners inserted at deterministic or pseudo-random positions.

Types of Characters Detected

Invisible characters: Zero-width spaces, joiners, word joiners, BOMs, soft hyphens, Hangul fillers, and Unicode tag characters
Directional override characters: Text-direction controls that can disguise filenames, URLs, or code
Typographic characters: Smart quotes, apostrophes, em/en dashes, and Unicode ellipsis
Special whitespace: Non-breaking, en, em, thin, and hair spaces

How Binary Watermarking Works

Advanced systems encode binary data using invisible characters:

Zero-width space represents binary 0
Zero-width non-joiner represents binary 1

Sequences of these characters can encode identifiers, timestamps, model versions, or session metadata. The detector identifies these patterns and estimates their density.

AI Pattern Analysis

Beyond hidden characters, the tool evaluates higher-level AI writing patterns:

Spacing consistency: Unnaturally uniform paragraph and line spacing
Paragraph structure: Repeated lengths and similar opening phrases
Typographic preference: Consistent use of non-ASCII typography

These signals are secondary to watermark detection but strengthen attribution when combined.

How to Use the Tool

Paste text directly into the input area (note: some browsers sanitize invisible characters)
Upload a .txt or .md file for the most reliable results

Analysis Modes

Quick Scan: Invisible and special Unicode characters only
Full Analysis: Includes binary watermark detection, structural analysis, and AI model estimation

Typography Normalization

When enabled, the cleaned output converts typographic characters to plain ASCII. Re-analyzing normalized text results in a zero fingerprint score.

Understanding the Results

AI Fingerprint Score (0–100)

Higher scores indicate stronger evidence of hidden watermarks or AI fingerprints. Invisible characters and binary encodings carry the most weight; typography alone has minimal impact.

Detection Categories

Results are grouped into invisible characters, binary watermarks, typographic markers, structural patterns, and probable AI model attribution.

Cleaned Text Output

The cleaned version removes hidden characters, normalizes whitespace, preserves structure, and optionally normalizes typography. The output can be downloaded instantly.

Common Use Cases

Verifying whether content contains AI tracking markers
Academic and professional integrity checks
Security audits for directional override abuse
Privacy protection before sharing or publishing text
Detecting AI-generated plagiarism, spam