Dataset Ownership Verification

Dataset ownership verification aims to protect the intellectual property of datasets used to train machine learning models, particularly in the context of open-source or commercially available data. Current research focuses on watermarking techniques, often employing backdoor methods to subtly embed ownership information within the data or trained models, enabling verification even in black-box settings. These methods are being refined to improve stealth and robustness against removal attempts, while also addressing concerns about potential harm introduced by the watermarking process itself. The development of effective and harmless verification techniques is crucial for fostering responsible data usage and promoting fair practices in the rapidly expanding field of artificial intelligence.