Applying differential privacy to computer vision, especially with datasets including photos or videos, involves carefully designing privacy-preserving mechanisms. While direct methods like blurring faces might seem straightforward, they are not always practical or sufficient. Instead, differential privacy can be implemented in various stages of the data processing pipeline. For example, noise can be added to the output of models trained on image data, preserving privacy in the inference stage. Alternatively, during model training, techniques like differentially private stochastic gradient descent can be used to ensure that the trained models do not retain or reveal sensitive details about the individuals in the training dataset.