The field of intellectual property protection in AI-generated content is rapidly advancing, with a focus on developing innovative methods for detecting and preventing piracy, tampering, and misinformation. Recent research has explored the use of higher-order statistics, chaotic mapping, and diffusion models to create robust and discriminative hashes for copyright protection and integrity verification. Additionally, there is a growing interest in developing forensic frameworks for identifying AI-generated images and videos, including techniques such as diffusion snap-back reconstruction and frequency forgery clues. These advancements have significant implications for the protection of intellectual property and the prevention of malicious use of AI-generated content. Noteworthy papers in this area include:

• A lightweight CNN model hashing technique that integrates higher-order statistics features with a chaotic mapping mechanism for efficient piracy detection and precise tampering localization.
• A deep neural watermarking framework for 3D point cloud copyright protection and ownership verification that leverages the extraction capabilities of Deep Learning using PointNet++ neural network architecture.
• A diffusion-based forensic framework that identifies AI-generated images by analyzing reconstruction metrics across varying noise strengths.
• A method for detecting generated images by fitting natural image distributions and exploiting geometric differences between the data manifolds of natural and generated images.