Meta Seal: State-of-the-Art Open Source Invisible Watermarking

Meta Seal: Invisible Watermarking for the AI Era

As generative AI becomes ubiquitous, verifying content authenticity and provenance is increasingly critical. Meta Seal is a comprehensive, open-source framework that provides state-of-the-art invisible watermarking across all modalities-audio, image, video, and text. Developed by Meta’s research team, this suite spans the entire generative AI lifecycle from training data to generated media.

Whether you’re a researcher, developer, or content creator, Meta Seal gives you the tools to protect and authenticate digital content.

Key Features

🎬 Post-Hoc Watermarking

Apply watermarks after content generation-model-agnostic and universal across all content types.

Image & Video Models:

PixelSeal 🏆 - Flagship image & video watermarking model, SOTA in robustness and imperceptibility
ChunkySeal - 4× capacity boost to 1024 bits while preserving quality
VideoSeal - Extension to video, resilient to editing and codecs
WAM - Embed localized watermarks that survive inpainting and splicing
SyncSeal - Revert geometric transformations applied to images

Audio Models:

AudioSeal & AudioSeal Streaming - Localized audio watermarking with sample-level detection and real-time streaming support

Text Models:

TextSeal - Comprehensive evaluation framework for post-hoc text watermarking with LLM rephrasing

🔄 In-Model & Generation Time Watermarking

Embed watermarks during content generation by modifying model behavior or latent representations:

DISTSEAL - Unified latent space watermarking with 20× speedup over pixel methods
Stable Signature - Roots watermarks in the model’s latent decoder for tracing outputs
WMAR - Watermarking for autoregressive image generation models

📊 Dataset Watermarking

Embed watermarks into training datasets to track data provenance:

Radioactive Watermarks - Detect if LLMs were trained on synthetic text, with high confidence even at 5% watermark rate
Benchmark Contamination Detection - Watermark benchmarks to detect if models were trained on test sets

🔒 Watermark Security

Research on adversarial attacks and defenses:

WMForger - Black-box watermark forging for red-teaming watermarking systems

Platforms

Meta Seal’s components are designed to run on:

🐧 Linux
🍎 macOS
🪟 Windows

Individual models may have specific requirements (Python, PyTorch, etc.).

Get Started

Each component of Meta Seal is available as a separate repository with its own installation instructions. Visit the GitHub organization to explore individual models.

🔗 Website: facebookresearch.github.io/meta-seal

🔗 GitHub: github.com/facebookresearch/meta-seal

Why This Tool Rocks

Comprehensive Coverage: Watermarking for every modality-images, video, audio, and text-all in one framework
Research-Backed: Built by Meta’s research team with published papers for each component
Truly Open Source: MIT licensed, free to use, modify, and integrate into your projects
Production Ready: Includes streaming support for real-time applications and robustness against common attacks
AI Lifecycle Coverage: From dataset watermarking to post-generation marking-secure content at every stage

Crepi il lupo! 🐺