Intellectual Property Protection

Intellectual property protection (IPP) for machine learning models, particularly deep neural networks (DNNs) and large language models (LLMs), is a critical area of research driven by the increasing economic value of these models. Current efforts focus on developing robust watermarking and fingerprinting techniques, often leveraging generative adversarial networks (GANs) or employing methods like mask pruning and weight obfuscation to deter unauthorized access and replication. These advancements are crucial for fostering innovation in AI by ensuring fair compensation for developers and preventing the misuse of proprietary models, with implications for various sectors including broadcasting, creative industries, and cybersecurity.