CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

By Maxim Maximov, Ismail Elezi, L. Leal-Taix'e. 2020

234 citations recorded by the source corpus.

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results. To facilitate further research, we make available the code and the models at https://github.com/dvl-tum/ciagan.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Bo Li
  2. Bowen Zhou
  3. Shuai Di
  4. Jingen Liu
  5. Jiquan Pei
  6. Bo Liu
  7. Jinfeng Yi
  8. Peng Qi

In Generative Models

  1. Bo Li
  2. Bowen Zhou

In Privacy & Security

  1. Bo Li
  2. Jinfeng Yi

Citations in the corpus