Fingerprinting Lemma

Fingerprinting, in the context of machine learning models, aims to uniquely identify the source or origin of generated content, often to protect intellectual property or detect malicious use. Current research focuses on developing robust and efficient fingerprinting techniques for large language models (LLMs) and generative models like GANs and diffusion models, employing methods such as vector addition, evolutionary learning, and analysis of embedding space characteristics. These techniques are crucial for addressing concerns around model attribution, security, and combating the spread of misinformation, with applications ranging from copyright protection to identifying deepfakes.