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Text Provenance and Watermarking Systems for NLP Applications

7 chapters · 32 lessons

1. 1. Tokenization and Representation for Provenance Analysis

   4 lessons

   Build the foundational text processing pipeline that enables both watermarking and detection systems.

2. 2. Statistical Foundations of Watermark Detection

   5 lessons

   Master the probability theory and hypothesis testing that underpin reliable detection systems.

3. 3. Designing Topic-Aware Watermarking Schemes

   5 lessons

   Implement watermarking methods that embed detectable signals while preserving content quality and meaning.

4. 4. Building Detection Pipelines and Classifiers

   5 lessons

   Construct end-to-end systems that identify watermarked content and estimate provenance confidence.

5. 5. Robustness, Attacks, and System Hardening

   4 lessons

   Evaluate vulnerabilities in provenance systems and design defenses against evasion and degradation.

6. 6. Production Integration and Data Governance Workflows

   5 lessons

   Deploy provenance systems within content pipelines, training workflows, and compliance frameworks.

7. 7. Evaluation, Benchmarking, and Continuous Improvement

   4 lessons

   Establish metrics and testing protocols to measure and improve provenance system performance over time.