The field of multimedia security and authentication is rapidly evolving, with a focus on developing innovative techniques to protect digital content from unauthorized access and manipulation. Recent research has explored the use of steganography, watermarking, and forgery detection methods to ensure the integrity and authenticity of multimedia data. Notably, the integration of artificial intelligence and machine learning algorithms has improved the robustness and accuracy of these methods. Furthermore, the development of robust and non-intrusive watermarking techniques has addressed concerns about the potential impact of watermarking on model behavior and performance. Overall, these advancements have significant implications for the protection of intellectual property and the prevention of misinformation in the digital landscape. Noteworthy papers include: The paper introducing IConMark presents a novel in-generation robust semantic watermarking method for AI-generated images, demonstrating superior detection accuracy and maintaining image quality. The paper proposing SEER introduces a novel network for multimodal fake news detection, utilizing semantic enhancement and emotional reasoning to achieve state-of-the-art results on real-world datasets.