The field of intellectual property protection for AI-generated content is rapidly evolving, with a focus on developing robust and detectable watermarking methods. Recent developments have shifted towards creating visible watermarks that are hard to remove, as well as using cryptographic techniques such as chameleon hash functions to achieve strong model ownership claims. Another area of research is focused on modelship attribution, which aims to trace the evolution of manipulated images by identifying the generative models involved and reconstructing the sequence of edits they performed. Additionally, researchers are exploring the use of neural fingerprinting for text-to-image diffusion models to mitigate the risk of misuse. Notable papers in this area include: KGMark, which proposes a graph watermarking framework for dynamic knowledge graphs. MUSE, which introduces a model-agnostic tabular watermarking algorithm via multi-sample selection. CHIP, which proposes a Chameleon Hash-based Irreversible Passport protection framework for robust deep model ownership verification. Modelship Attribution, which introduces a novel method to trace multi-stage manipulations across generative models. Beyond Invisibility, which proposes a universal approach that embeds visible watermarks into images for stronger copyright protection. PALADIN, which proposes a robust neural fingerprinting method for text-to-image diffusion models.