A Comparative Study on the Privacy Risks of Face Recognition Libraries

  • István Fábián Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Hungary https://orcid.org/0000-0003-0293-0335
  • Gábor György Gulyás Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Hungary https://orcid.org/0000-0003-0877-0088
Keywords: face recognition, machine learning, privacy

Abstract

The rapid development of machine learning and the decreasing costs of computational resources has led to a widespread usage of face recognition. While this technology offers numerous benefits, it also poses new risks. We consider risks related to the processing of face embeddings, which are floating point vectors representing the human face in an identifying way. Previously, we showed that even simple machine learning models are capable of inferring demographic attributes from embeddings, leading to the possibility of re-identification attacks. This paper examines three popular Python libraries for face recognition, comparing their face detection performance and inspecting how much risk each library's embeddings pose regarding the aforementioned data leakage. Our experiments were conducted on a balanced face image dataset of different sexes and races, allowing us to discover biases in our results.

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Published
2021-08-04
How to Cite
Fábián, I., & Gulyás, G. G. (2021). A Comparative Study on the Privacy Risks of Face Recognition Libraries. Acta Cybernetica, 25(2), 233-255. https://doi.org/10.14232/actacyb.289662
Section
Special Issue of the 12th Conference of PhD Students in Computer Science