Balancing Privacy and Utility: An Evaluation of Generative Models for Car Anonymization in Street Scene Images

Abstract. When recording street scenes, the privacy of the people concerned must always be taken into account. Previous work has focused heavily on directly identifiable features such as faces or license plates. However, the visibility of indirectly identifiable objects can also limit the protection of citizens. This is especially true for cars. Regular recordings could be used to infer when a person is at home or where else they have been. The obvious solution would be to simply make cars unrecognizable. However, previous work has shown that this has a negative impact on downstream tasks such as traffic sign recognition. Therefore, in this work, we use generative models to synthetically modify cars. We compare models from the generative adversarial network (DP-GAN, OASIS) and diffusion model communities (Kolors, SDXL) to see which ones are best suited in terms of anonymity, image integrity, and performance. For the GAN-based models, we use an image-to-image translation approach to modify only image sections with cars, while for the diffusion models, we develop two methods that use text-guided image inpainting. We compare the developed methods using established metrics and perform a survey with test subjects. In terms of anonymity, all models achieve convincing results, while diffusion models that generate each car mask individually produce particularly realistic images. In return, GAN-based methods process images more than twice as fast creating a trade-off between image integrity and performance.