Robust Watermarking and Copyright Protection in AI and Digital Content

The recent advancements in digital watermarking and copyright protection for machine learning models and digital content have shown a significant shift towards more robust and versatile solutions. The field is moving towards developing watermarking techniques that are resistant to various attacks, including those that exploit the unique characteristics of different data types such as text, images, and graphs. Notably, there is a growing emphasis on creating watermarking schemes that can operate in black-box settings, ensuring that models remain protected even when their internal workings are not fully known. Additionally, the integration of watermarking with federated learning paradigms is emerging as a critical area, addressing the challenges of model theft and unauthorized usage in decentralized learning environments. Furthermore, the robustness of watermarks against advanced image editing techniques and the protection of open-source language models are areas that are receiving considerable attention. These developments collectively indicate a trend towards more secure and resilient watermarking methods that can adapt to the evolving landscape of AI and digital content protection.

Noteworthy papers include:

  • 'NSmark: Null Space Based Black-box Watermarking Defense Framework for Pre-trained Language Models' introduces a novel black-box watermarking scheme resistant to Last-Layer Linear Functionality Equivalence Attacks.
  • 'SLIC: Secure Learned Image Codec through Compressed Domain Watermarking to Defend Image Manipulation' presents an active approach to image authenticity through watermarking in the compressed domain.
  • 'FedGMark: Certifiably Robust Watermarking for Federated Graph Learning' proposes the first certified robust backdoor-based watermarking for federated graph learning.

Sources

NSmark: Null Space Based Black-box Watermarking Defense Framework for Pre-trained Language Models

Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models

SLIC: Secure Learned Image Codec through Compressed Domain Watermarking to Defend Image Manipulation

SoK: Dataset Copyright Auditing in Machine Learning Systems

FedGMark: Certifiably Robust Watermarking for Federated Graph Learning

Securing Federated Learning Against Novel and Classic Backdoor Threats During Foundation Model Integration

ESpeW: Robust Copyright Protection for LLM-based EaaS via Embedding-Specific Watermark

Neural Cover Selection for Image Steganography

ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks

Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances

Provably Robust Watermarks for Open-Source Language Models

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