The field of steganography is moving towards more innovative and effective methods of concealing secret information in various types of data, including text, images, audio, and videos. Researchers are exploring new techniques, such as character-based diffusion embedding algorithms and implicit neural representation, to improve the quality and security of steganographic materials. These advancements have the potential to enhance data security and privacy in various applications. Noteworthy papers in this area include: A Character-based Diffusion Embedding Algorithm for Enhancing the Generation Quality of Generative Linguistic Steganographic Texts, which proposes a novel embedding algorithm that leverages sensitive information's properties to improve the quality of generated steganographic text. Unified Steganography via Implicit Neural Representation, which presents a novel method for steganography that uses implicit neural representation to conceal secret data across different data types. RFNNS: Robust Fixed Neural Network Steganography with Popular Deep Generative Models, which introduces a robust fixed neural network steganography method that enhances the security and practicality of steganography.