The field of deep learning security and watermarking is rapidly evolving, with a focus on developing robust and secure methods for protecting intellectual property and detecting malicious activities. Recent research has explored innovative approaches to neural network watermarking, including the use of frequency components and cryptographic chains to create secure and robust watermarks. Additionally, there have been significant advancements in deepfake detection, with new methods leveraging coarse-to-fine spatial information, semantic information, and feature orthogonality to improve generalization and detection capabilities. Noteworthy papers in this area include those proposing novel watermarking schemes, such as ChainMarks, and those introducing new deepfake detection strategies, such as Cross-Branch Orthogonality and RealID. These developments have the potential to significantly impact the field, enabling more effective protection of deep learning models and detection of malicious activities.