Watermarking Loss

Watermarking loss research focuses on embedding imperceptible identifiers, or watermarks, into machine learning models to protect intellectual property. Current efforts concentrate on developing robust watermarking techniques for various model architectures, including Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs), that withstand attacks like model extraction and fine-tuning. These methods often involve adding a specialized loss function during model training, aiming to create watermarks resistant to modification while minimally impacting model performance. The success of these techniques is crucial for safeguarding the ownership and integrity of valuable AI models and their outputs.