Watermarking involves embedding a hidden signal into digital media like text, images, and audio to establish ownership or ensure authenticity. It has become increasingly important in the age of generative AI. However, despite its growing significance, watermarking in the AI community often gets lost in broader conversations around adversarial robustness, and general security, and safety. We argue that watermarking needs its own dedicated space in AI conferences for discussion and exploration, where researchers can dig deeper into the technical specifics of this field and build on a foundation of research spanning over 20 years. The aim of this workshop is to bring together experts from academia, industry, policy and from different communities to discuss advancements and challenges in watermarking technologies. The event will facilitate the exchange of ideas and collaborative problem-solving.

Topics of interest include, but are not limited to:
- Algorithmic Advances: Multi-modal watermarking, model watermarking, dataset tracing and attribution.
- Watermark Security: Theoretical results on strong watermark impossibility, black and white-box adversarial attacks, advanced threat models, open-sourced and publicly detectable watermarking, and zero-knowledge watermarking.
- Evaluation: Benchmarks for watermarking, perceptual models and watermark-specific quality evaluation metrics, and bias in watermarking robustness.
- Industry Requirements: Large bit watermarking, low FPRs, and complexities of deployment in-the-wild.
- Policy and Ethics: Dual use, communication to policy makers, and standards.
- Explainability and Interpretability: Understanding how watermarks work and their limitations, human oversight and review, and balancing automation with human judgment.