The field of artificial intelligence is moving towards developing more robust and secure methods for protecting intellectual property and ensuring privacy. Researchers are exploring innovative approaches to watermarking and steganography, such as subspace-anchored watermarks and content-preserving linguistic steganography, to protect AI models from unauthorized use and maintain the integrity of sensitive information. Additionally, there is a growing focus on protecting geo-privacy and developing secure semantic communication systems. Notable papers in this area include: SEAL, which proposes a subspace-anchored watermarking framework for large language models, and CLstega, which introduces a content-preserving linguistic steganography paradigm for perfectly secure covert communication. RegionMarker is also noteworthy, as it presents a region-triggered semantic watermarking framework for embedding-as-a-service copyright protection. TopoReformer is another significant contribution, which mitigates adversarial attacks using topological purification in OCR models.