The recent advancements in the research area of adversarial techniques and watermarking for digital media have shown significant progress in enhancing both the security and aesthetic qualities of generated content. The field is moving towards more sophisticated and practical solutions that balance the need for robustness, imperceptibility, and user customization. Innovations in generative models, particularly those leveraging diffusion and adversarial networks, are paving the way for more naturalistic and customizable adversarial patches and watermarking techniques. These methods are not only improving the visual quality of the output but also enhancing the security and robustness against various attacks. Additionally, the integration of 3D Gaussian splatting with watermarking and adversarial techniques is opening new avenues for protecting 3D assets and exploring vulnerabilities in 3D models. The noteworthy papers in this area include one that introduces a novel diffusion-based customizable patch generation framework, another that proposes an innovative and efficient framework for watermarking 3D Gaussian splatting assets, and a third that investigates adversarial noise in 3D objects, highlighting the need for robust defenses in critical applications.