Availability Attack

Availability attacks aim to render training datasets unusable for machine learning models by introducing subtle, imperceptible perturbations, thereby protecting data privacy or hindering unauthorized model training. Current research focuses on developing more effective attack strategies, particularly for diverse data types like images and 3D point clouds, and exploring robust defenses, including adversarial training and data compression techniques. This area is crucial for advancing data security and privacy in machine learning, with implications for both theoretical understanding of model robustness and practical applications involving sensitive datasets.